Used Cars Price Prediction

Context

There is a huge demand for used cars in the Indian Market today. As sales of new cars have slowed down in the recent past, the pre-owned car market has continued to grow over the past years and is larger than the new car market now. Cars4U is a budding tech start-up that aims to find footholes in this market.

In 2018-19, while new car sales were recorded at 3.6 million units, around 4 million second-hand cars were bought and sold. There is a slowdown in new car sales and that could mean that the demand is shifting towards the pre-owned market. In fact, some car sellers replace their old cars with pre-owned cars instead of buying new ones. Unlike new cars, where price and supply are fairly deterministic and managed by OEMs (Original Equipment Manufacturer / except for dealership level discounts which come into play only in the last stage of the customer journey), used cars are very different beasts with huge uncertainty in both pricing and supply. Keeping this in mind, the pricing scheme of these used cars becomes important in order to grow in the market.

As a senior data scientist at Cars4U, you have to come up with a pricing model that can effectively predict the price of used cars and can help the business in devising profitable strategies using differential pricing. For example, if the business knows the market price, it will never sell anything below it.

Objectives

To explore and visualize the dataset, build a pricing model (Linear Regression) that can effectively predict the price of used cars and generate a set of insights and recommendations that can help the business in devising profitable strategies. For example, if the business knows the market price, it will never sell anything below it.

Key Questions

1. What are the factors that influence the selling price of a used car?
2. What is a good predictive model that outputs the selling price of a used car?

Data Description

The data contains the different attributes of used cars sold in different locations. The detailed data dictionary is given below.

Data Dictionary

• S.No.: Serial number
• Name: Name of the car which includes brand name and model name
• Location: Location in which the car is being sold or is available for purchase (cities)
• Year: Manufacturing year of the car
• Kilometers_driven: The total kilometers (a unit used to measure length or distance) driven in the car by the previous owner(s) -Fuel_Type: The type of fuel used by the car (Petrol, Diesel, Electric, CNG, LPG) -Transmission: The type of transmission used by the car (Automatic/Manual) -Owner: Type of ownership -Mileage: The standard mileage offered by the car company in kmpl or km/kg -Engine: The displacement volume of the engine in CC -Power: The maximum power of the engine in bhp -Seats: The number of seats in the car -New_Price: The price of a new car of the same model in INR Lakhs (1 Lakh INR = 100,000 INR) -Price: The price of the used car in INR Lakhs

Setup

Importing Libraries

#import libraries and packages to help with reading and manipulating data
import pandas as pd
import numpy as np

#Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

sns.set()

# to split the data into train and test
from sklearn.model_selection import train_test_split

# to build linear regression_model
from sklearn.linear_model import LinearRegression

# to check model performance
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

# Removes the limit from the number of displayed columns and rows.
# This is so I can see the entire dataframe when I print it
pd.set_option('display.max_columns', None)
# pd.set_option('display.max_rows', None)
pd.set_option('display.max_rows', 200)

#restrict float value to 3 decimal places
pd.set_option('display.float_format',lambda x: '%.2f' % x)
%matplotlib inline

#ignoring warnings
import warnings
warnings.filterwarnings("ignore")

#allow access to google drive
from google.colab import drive
drive.mount('/content/gdrive')

Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).

#load dataset and store into a dataframe
usedcar_df = pd.read_csv("/content/gdrive/MyDrive/UT-ML/Projects/Project2/used_cars_data.csv")

#make a separate copy of the original dataframe
usedcarworks_df = usedcar_df.copy()

EDA

#Check shape of the data: Number of rows and columns
print(f"There are {usedcar_df.shape[0]} rows and {usedcar_df.shape[1]} columns.")

There are 7253 rows and 14 columns.

#head and tail of the dataframe to get an idea of the data
usedcarworks_df.head()

	S.No.	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price
0	0	Maruti Wagon R LXI CNG	Mumbai	2010	72000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	1.75
1	1	Hyundai Creta 1.6 CRDi SX Option	Pune	2015	41000	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.00	16.06	12.50
2	2	Honda Jazz V	Chennai	2011	46000	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.00	8.61	4.50
3	3	Maruti Ertiga VDI	Chennai	2012	87000	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.00	11.27	6.00
4	4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	2013	40670	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.00	53.14	17.74

usedcarworks_df.tail()

	S.No.	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price
7248	7248	Volkswagen Vento Diesel Trendline	Hyderabad	2011	89411	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	NaN
7249	7249	Volkswagen Polo GT TSI	Mumbai	2015	59000	Petrol	Automatic	First	17.21 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN
7250	7250	Nissan Micra Diesel XV	Kolkata	2012	28000	Diesel	Manual	First	23.08 kmpl	1461 CC	63.1 bhp	5.00	9.47	NaN
7251	7251	Volkswagen Polo GT TSI	Pune	2013	52262	Petrol	Automatic	Third	17.2 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN
7252	7252	Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...	Kochi	2014	72443	Diesel	Automatic	First	10.0 kmpl	2148 CC	170 bhp	5.00	86.97	NaN

# Lets look at 15 random rows
# Set the random seed via np.random.seed so that
# we produce the same random results every time
np.random.seed(1)
usedcarworks_df.sample(n=15)

	S.No.	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price
2397	2397	Ford EcoSport 1.5 Petrol Trend	Kolkata	2016	21460	Petrol	Manual	First	17.0 kmpl	1497 CC	121.36 bhp	5.00	9.47	6.00
3777	3777	Maruti Wagon R VXI 1.2	Kochi	2015	49818	Petrol	Manual	First	21.5 kmpl	1197 CC	81.80 bhp	5.00	5.44	4.11
4425	4425	Ford Endeavour 4x2 XLT	Hyderabad	2007	130000	Diesel	Manual	First	13.1 kmpl	2499 CC	141 bhp	7.00	35.29	6.00
3661	3661	Mercedes-Benz E-Class E250 CDI Avantgrade	Coimbatore	2016	39753	Diesel	Automatic	First	13.0 kmpl	2143 CC	201.1 bhp	5.00	86.97	35.28
4514	4514	Hyundai Xcent 1.2 Kappa AT SX Option	Kochi	2016	45560	Petrol	Automatic	First	16.9 kmpl	1197 CC	82 bhp	5.00	8.23	6.34
599	599	Toyota Innova Crysta 2.8 ZX AT	Coimbatore	2019	40674	Diesel	Automatic	First	11.36 kmpl	2755 CC	171.5 bhp	7.00	28.05	24.82
186	186	Mercedes-Benz E-Class E250 CDI Avantgrade	Bangalore	2014	37382	Diesel	Automatic	First	13.0 kmpl	2143 CC	201.1 bhp	5.00	86.97	32.00
305	305	Audi A6 2011-2015 2.0 TDI Premium Plus	Kochi	2014	61726	Diesel	Automatic	First	17.68 kmpl	1968 CC	174.33 bhp	5.00	67.10	20.77
4582	4582	Hyundai i20 1.2 Magna	Kolkata	2011	36000	Petrol	Manual	First	18.5 kmpl	1197 CC	80 bhp	5.00	10.25	2.50
5434	5434	Honda WR-V Edge Edition i-VTEC S	Kochi	2019	13913	Petrol	Manual	First	17.5 kmpl	1199 CC	88.7 bhp	5.00	9.36	8.20
3042	3042	Hyundai Verna 1.6 VTVT	Kolkata	2011	36226	Petrol	Manual	First	17.01 kmpl	1591 CC	121.3 bhp	5.00	14.26	2.95
503	503	Toyota Fortuner 4x4 AT	Delhi	2017	25000	Diesel	Automatic	First	12.55 kmpl	2982 CC	168.5 bhp	7.00	37.17	32.00
1038	1038	Land Rover Range Rover TDV8 (Diesel)	Bangalore	2011	55000	Diesel	Automatic	First	10.2 kmpl	4367 CC	308 bhp	7.00	120.00	67.00
4543	4543	Maruti Alto K10 VXI AGS	Chennai	2017	5500	Petrol	Automatic	First	24.07 kmpl	998 CC	67.1 bhp	5.00	4.36	4.25
2328	2328	BMW X5 xDrive 30d M Sport	Chennai	2017	6500000	Diesel	Automatic	First	15.97 kmpl	2993 CC	258 bhp	5.00	55.07	65.00

Observations

• Seems like S.No is actually an index number. We can drop it later.
• Mileage is in different units (i.e. km/kg, kmpl)
• Engine size, Mileage, Power have numerical values and text

#general info, check data types and number of non-null values
usedcarworks_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7253 entries, 0 to 7252
Data columns (total 14 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   S.No.              7253 non-null   int64  
 1   Name               7253 non-null   object 
 2   Location           7253 non-null   object 
 3   Year               7253 non-null   int64  
 4   Kilometers_Driven  7253 non-null   int64  
 5   Fuel_Type          7253 non-null   object 
 6   Transmission       7253 non-null   object 
 7   Owner_Type         7253 non-null   object 
 8   Mileage            7251 non-null   object 
 9   Engine             7207 non-null   object 
 10  Power              7078 non-null   object 
 11  Seats              7200 non-null   float64
 12  New_Price          7253 non-null   float64
 13  Price              6019 non-null   float64
dtypes: float64(3), int64(3), object(8)
memory usage: 793.4+ KB

Notice that there are 8 object types.

Name and Location can stay as objects.

Fuel Type, Transmission, Owner_Type, can be converted to dummy values.

Mileage, Engine, and Power can be converted to numerical values.

Notice that Price, Mileage, Engine, Power, and Seats have missing values.

We will see later in our analysis how to handle those features.

# sorting by amount of missing values
usedcarworks_df.isnull().sum().sort_values(ascending=False)

Price                1234
Power                 175
Seats                  53
Engine                 46
Mileage                 2
S.No.                   0
Name                    0
Location                0
Year                    0
Kilometers_Driven       0
Fuel_Type               0
Transmission            0
Owner_Type              0
New_Price               0
dtype: int64

Features 'Engine', 'Mileage', 'Power', 'Seats', 'Price' have null values.

Price is the feature with most missing values.

These have to be addressed during our analysis.

#Duplicate Entries
usedcarworks_df.duplicated().sum()

0

There are no duplicated entries.

#Lets look at the statistical summary of the data at this point
usedcarworks_df.describe().T

	count	mean	std	min	25%	50%	75%	max
S.No.	7253.00	3626.00	2093.91	0.00	1813.00	3626.00	5439.00	7252.00
Year	7253.00	2013.37	3.25	1996.00	2011.00	2014.00	2016.00	2019.00
Kilometers_Driven	7253.00	58699.06	84427.72	171.00	34000.00	53416.00	73000.00	6500000.00
Seats	7200.00	5.28	0.81	0.00	5.00	5.00	5.00	10.00
New_Price	7253.00	21.31	24.26	3.91	7.88	11.30	21.69	375.00
Price	6019.00	9.48	11.19	0.44	3.50	5.64	9.95	160.00

Notice that as the data stands, the average of km driven is 58,699 and a maximum of 6'500,000 km were driven. This is very high and unusual number of km in any car odometer. 75% of cars have at 73,000 or less km and a mean of 58,699km.

There is also a very high new_price of INR 37'500,000. 75% of cars have a new price of INR 2'131.000. Is the highest new_price possible?

75% of cars have a used selling price of INR 995,000 a highest price of INR 16'000,000, and a mean price of INR 948,000. Since the highest new price is INR 37'500,000, we can assume that the highest used price of INR 16'000,000 may be possible if the first one is good.

We will look at each feature on detail next.

Univariate Analysis

S.No.

usedcarworks_df['S.No.']

0          0
1          1
2          2
3          3
4          4
        ... 
7248    7248
7249    7249
7250    7250
7251    7251
7252    7252
Name: S.No., Length: 7253, dtype: int64

S.No. is the Serial Number of the item on the dataset. This is actually the index of the item.

Since we have an index, this column can be dropped.

Next action: Drop the column.

#Drop S.No.
usedcarworks_df['S.No.']
usedcarworks_df = usedcarworks_df.drop(['S.No.'], axis=1)
usedcarworks_df

	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price
0	Maruti Wagon R LXI CNG	Mumbai	2010	72000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	1.75
1	Hyundai Creta 1.6 CRDi SX Option	Pune	2015	41000	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.00	16.06	12.50
2	Honda Jazz V	Chennai	2011	46000	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.00	8.61	4.50
3	Maruti Ertiga VDI	Chennai	2012	87000	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.00	11.27	6.00
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	2013	40670	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.00	53.14	17.74
...	...	...	...	...	...	...	...	...	...	...	...	...	...
7248	Volkswagen Vento Diesel Trendline	Hyderabad	2011	89411	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	NaN
7249	Volkswagen Polo GT TSI	Mumbai	2015	59000	Petrol	Automatic	First	17.21 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN
7250	Nissan Micra Diesel XV	Kolkata	2012	28000	Diesel	Manual	First	23.08 kmpl	1461 CC	63.1 bhp	5.00	9.47	NaN
7251	Volkswagen Polo GT TSI	Pune	2013	52262	Petrol	Automatic	Third	17.2 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN
7252	Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...	Kochi	2014	72443	Diesel	Automatic	First	10.0 kmpl	2148 CC	170 bhp	5.00	86.97	NaN

7253 rows × 13 columns

#visualizing our 15 random rows
np.random.seed(1)
usedcarworks_df.sample(n=15)

	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price
2397	Ford EcoSport 1.5 Petrol Trend	Kolkata	2016	21460	Petrol	Manual	First	17.0 kmpl	1497 CC	121.36 bhp	5.00	9.47	6.00
3777	Maruti Wagon R VXI 1.2	Kochi	2015	49818	Petrol	Manual	First	21.5 kmpl	1197 CC	81.80 bhp	5.00	5.44	4.11
4425	Ford Endeavour 4x2 XLT	Hyderabad	2007	130000	Diesel	Manual	First	13.1 kmpl	2499 CC	141 bhp	7.00	35.29	6.00
3661	Mercedes-Benz E-Class E250 CDI Avantgrade	Coimbatore	2016	39753	Diesel	Automatic	First	13.0 kmpl	2143 CC	201.1 bhp	5.00	86.97	35.28
4514	Hyundai Xcent 1.2 Kappa AT SX Option	Kochi	2016	45560	Petrol	Automatic	First	16.9 kmpl	1197 CC	82 bhp	5.00	8.23	6.34
599	Toyota Innova Crysta 2.8 ZX AT	Coimbatore	2019	40674	Diesel	Automatic	First	11.36 kmpl	2755 CC	171.5 bhp	7.00	28.05	24.82
186	Mercedes-Benz E-Class E250 CDI Avantgrade	Bangalore	2014	37382	Diesel	Automatic	First	13.0 kmpl	2143 CC	201.1 bhp	5.00	86.97	32.00
305	Audi A6 2011-2015 2.0 TDI Premium Plus	Kochi	2014	61726	Diesel	Automatic	First	17.68 kmpl	1968 CC	174.33 bhp	5.00	67.10	20.77
4582	Hyundai i20 1.2 Magna	Kolkata	2011	36000	Petrol	Manual	First	18.5 kmpl	1197 CC	80 bhp	5.00	10.25	2.50
5434	Honda WR-V Edge Edition i-VTEC S	Kochi	2019	13913	Petrol	Manual	First	17.5 kmpl	1199 CC	88.7 bhp	5.00	9.36	8.20
3042	Hyundai Verna 1.6 VTVT	Kolkata	2011	36226	Petrol	Manual	First	17.01 kmpl	1591 CC	121.3 bhp	5.00	14.26	2.95
503	Toyota Fortuner 4x4 AT	Delhi	2017	25000	Diesel	Automatic	First	12.55 kmpl	2982 CC	168.5 bhp	7.00	37.17	32.00
1038	Land Rover Range Rover TDV8 (Diesel)	Bangalore	2011	55000	Diesel	Automatic	First	10.2 kmpl	4367 CC	308 bhp	7.00	120.00	67.00
4543	Maruti Alto K10 VXI AGS	Chennai	2017	5500	Petrol	Automatic	First	24.07 kmpl	998 CC	67.1 bhp	5.00	4.36	4.25
2328	BMW X5 xDrive 30d M Sport	Chennai	2017	6500000	Diesel	Automatic	First	15.97 kmpl	2993 CC	258 bhp	5.00	55.07	65.00

We successfully dropped the S.No. Column.

Name

This column is a string that contains the brand, model, and some other valuable information that may be used later, like Engine size.

Next action: Use the column to determine Engine size for some Engine.

usedcarworks_df['Name']

0                                  Maruti Wagon R LXI CNG
1                        Hyundai Creta 1.6 CRDi SX Option
2                                            Honda Jazz V
3                                       Maruti Ertiga VDI
4                         Audi A4 New 2.0 TDI Multitronic
                              ...                        
7248                    Volkswagen Vento Diesel Trendline
7249                               Volkswagen Polo GT TSI
7250                               Nissan Micra Diesel XV
7251                               Volkswagen Polo GT TSI
7252    Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...
Name: Name, Length: 7253, dtype: object

usedcarworks_df['Name'].value_counts()

Mahindra XUV500 W8 2WD                  55
Maruti Swift VDI                        49
Maruti Swift Dzire VDI                  42
Honda City 1.5 S MT                     39
Maruti Swift VDI BSIV                   37
                                        ..
Chevrolet Beat LT Option                 1
Skoda Rapid 1.6 MPI AT Elegance Plus     1
Ford EcoSport 1.5 TDCi Ambiente          1
Hyundai i10 Magna 1.1 iTech SE           1
Hyundai Elite i20 Magna Plus             1
Name: Name, Length: 2041, dtype: int64

There are too many different models described on the dataset.

Maybe Brand and model can be used to determine which models are the ones that have a higher selling price?

#Create a new column to write down the Brands of the cars
carbrand = usedcarworks_df['Name'].str.split(" ", n=1, expand=True)
usedcarworks_df['Brand'] = carbrand[0]

#How many cars per brand in the dataset?
print("There are ", str(usedcarworks_df['Brand'].nunique())," different car brands in the dataset")
usedcarworks_df['Brand'].value_counts()

There are  33  different car brands in the dataset

Maruti           1444
Hyundai          1340
Honda             743
Toyota            507
Mercedes-Benz     380
Volkswagen        374
Ford              351
Mahindra          331
BMW               312
Audi              285
Tata              228
Skoda             202
Renault           170
Chevrolet         151
Nissan            117
Land               67
Jaguar             48
Fiat               38
Mitsubishi         36
Mini               31
Volvo              28
Porsche            19
Jeep               19
Datsun             17
ISUZU               3
Force               3
Isuzu               2
Bentley             2
Smart               1
Ambassador          1
Lamborghini         1
Hindustan           1
OpelCorsa           1
Name: Brand, dtype: int64

Notice that "Land" should be "Land Rover"

#brand Land should be Land Rover, so fix that and check.
usedcarworks_df['Brand'] = usedcarworks_df['Brand'].replace({'Land':'Land Rover'}, regex=True)
usedcarworks_df['Brand'].value_counts()

Maruti           1444
Hyundai          1340
Honda             743
Toyota            507
Mercedes-Benz     380
Volkswagen        374
Ford              351
Mahindra          331
BMW               312
Audi              285
Tata              228
Skoda             202
Renault           170
Chevrolet         151
Nissan            117
Land Rover         67
Jaguar             48
Fiat               38
Mitsubishi         36
Mini               31
Volvo              28
Porsche            19
Jeep               19
Datsun             17
ISUZU               3
Force               3
Isuzu               2
Bentley             2
Smart               1
Ambassador          1
Lamborghini         1
Hindustan           1
OpelCorsa           1
Name: Brand, dtype: int64

#visualize the head of the dataset now.
usedcarworks_df.head()

	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand
0	Maruti Wagon R LXI CNG	Mumbai	2010	72000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	1.75	Maruti
1	Hyundai Creta 1.6 CRDi SX Option	Pune	2015	41000	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.00	16.06	12.50	Hyundai
2	Honda Jazz V	Chennai	2011	46000	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.00	8.61	4.50	Honda
3	Maruti Ertiga VDI	Chennai	2012	87000	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.00	11.27	6.00	Maruti
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	2013	40670	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.00	53.14	17.74	Audi

#Visualize the brand distribution
plt.figure(figsize=(15,8))
plt.xticks(rotation=90)
usedcarworks_df['Brand'].value_counts().plot(kind="bar")
plt.title("Used Car Brand Dataset Distribution", fontsize=15)
plt.show()

Maruti and Hyundai are the 2 brands that have the most cars in the dataset, followed far behind by Honda.

Next: Should we try to convert to dummies so we can see how brand is correlated to price?

Location

The city where the car is at. This feature may be useful in determining how a location makes cars less/more expensive.

#How many cars per brand in the dataset?
print("There are ", str(usedcarworks_df['Location'].nunique()), " different locations in the dataset.")
usedcarworks_df['Location'].value_counts()

There are  11  different locations in the dataset.

Mumbai        949
Hyderabad     876
Coimbatore    772
Kochi         772
Pune          765
Delhi         660
Kolkata       654
Chennai       591
Jaipur        499
Bangalore     440
Ahmedabad     275
Name: Location, dtype: int64

#Visualize the location distribution
plt.figure(figsize=(15,8))
plt.xticks(rotation=90)
usedcarworks_df['Location'].value_counts().plot(kind="bar")
plt.title("Used Car Location Dataset Distribution", fontsize=15)
plt.show()

Mumbai and Hyderabad are the locations where most of the used cars are located in the dataset.

We can add geolocation longitude and latitude and map the locations. Also becaause there are only 11 locations we may be find useful to use dummy values to determine if a location makes a used car more/less expensive.

Location Latitude Longitude

Mumbai 19.076 72.878

Hyderabad 17.385 78.487

Coimbatore 11.016 76.970

Kochi 20.028 79.123

Pune 18.520 73.857

Delhi 28.704 77.102

Kolkata 22.623 88.369

Chennai 13.0768 80.247

Jaipur 26.9112 75.787

Bangalore 12.9753 77.590

Ahmedabad 23.0271 72.593

If we have time, we will add these to our dataset

Year

The year of manufacture of the car is a number that by itself is categorical.

#What are the years of cars in the dataset?
usedcarworks_df['Year'].value_counts()

2015    929
2014    925
2016    886
2013    791
2017    709
2012    690
2011    579
2010    407
2018    361
2009    252
2008    207
2007    148
2019    119
2006     89
2005     68
2004     35
2003     20
2002     18
2001      8
2000      5
1998      4
1999      2
1996      1
Name: Year, dtype: int64

print("The latest year is ", str(usedcarworks_df['Year'].max()))

The latest year is  2019

#Visualize the yearly distribution
plt.figure(figsize=(15,8))
plt.xticks(rotation=90)
sns.histplot(x=usedcarworks_df['Year'])
plt.title("Used Car Year Dataset Distribution", fontsize=15)
plt.show()

How about we derive an Age feature from the year of the car. This would be more meaningful in the analysis.

#Assumption: Year of the dataset is 2020. Reason? Because the latest year as shown lines above is 2019. So lets assume that the year is 2020.
carage = 2020 - usedcarworks_df['Year']
usedcarworks_df['Age'] = carage

#visualize the head to see the changes
usedcarworks_df.head()

	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
0	Maruti Wagon R LXI CNG	Mumbai	2010	72000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	1.75	Maruti	10
1	Hyundai Creta 1.6 CRDi SX Option	Pune	2015	41000	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.00	16.06	12.50	Hyundai	5
2	Honda Jazz V	Chennai	2011	46000	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.00	8.61	4.50	Honda	9
3	Maruti Ertiga VDI	Chennai	2012	87000	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.00	11.27	6.00	Maruti	8
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	2013	40670	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.00	53.14	17.74	Audi	7

#visualize the tail to see changes
usedcarworks_df.tail()

	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
7248	Volkswagen Vento Diesel Trendline	Hyderabad	2011	89411	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	NaN	Volkswagen	9
7249	Volkswagen Polo GT TSI	Mumbai	2015	59000	Petrol	Automatic	First	17.21 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	5
7250	Nissan Micra Diesel XV	Kolkata	2012	28000	Diesel	Manual	First	23.08 kmpl	1461 CC	63.1 bhp	5.00	9.47	NaN	Nissan	8
7251	Volkswagen Polo GT TSI	Pune	2013	52262	Petrol	Automatic	Third	17.2 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	7
7252	Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...	Kochi	2014	72443	Diesel	Automatic	First	10.0 kmpl	2148 CC	170 bhp	5.00	86.97	NaN	Mercedes-Benz	6

Age of a car is one of the most imporant features that affect the selling price of a used car.

usedcarworks_df['Age'].value_counts()

5     929
6     925
4     886
7     791
3     709
8     690
9     579
10    407
2     361
11    252
12    207
13    148
1     119
14     89
15     68
16     35
17     20
18     18
19      8
20      5
22      4
21      2
24      1
Name: Age, dtype: int64

The oldest car is 24 years old, and 5 years old is the most common. We can drop the Year column.

#Drop Year
usedcarworks_df = usedcarworks_df.drop(['Year'], axis=1)
usedcarworks_df

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
0	Maruti Wagon R LXI CNG	Mumbai	72000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	1.75	Maruti	10
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.00	16.06	12.50	Hyundai	5
2	Honda Jazz V	Chennai	46000	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.00	8.61	4.50	Honda	9
3	Maruti Ertiga VDI	Chennai	87000	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.00	11.27	6.00	Maruti	8
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.00	53.14	17.74	Audi	7
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7248	Volkswagen Vento Diesel Trendline	Hyderabad	89411	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	NaN	Volkswagen	9
7249	Volkswagen Polo GT TSI	Mumbai	59000	Petrol	Automatic	First	17.21 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	5
7250	Nissan Micra Diesel XV	Kolkata	28000	Diesel	Manual	First	23.08 kmpl	1461 CC	63.1 bhp	5.00	9.47	NaN	Nissan	8
7251	Volkswagen Polo GT TSI	Pune	52262	Petrol	Automatic	Third	17.2 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	7
7252	Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...	Kochi	72443	Diesel	Automatic	First	10.0 kmpl	2148 CC	170 bhp	5.00	86.97	NaN	Mercedes-Benz	6

7253 rows × 14 columns

Year was dropped. Instead now we have Age.

Kilometers Driven

#lets see the distribution of the kilometers driven
plt.figure(figsize=(15,8))
sns.boxplot(x = usedcarworks_df["Kilometers_Driven"])
plt.title("Distribution of Odometer", fontsize=15)
plt.show()

We can see an outlier at over 6,000,000 km.

plt.figure(figsize=(15,8))
sns.histplot(data=usedcarworks_df ,x="Kilometers_Driven")
plt.title("Distribution of Odometer", fontsize=15)
plt.show()

The odometer of a car is always a very important feature that affects the selling price of a used car.

Next: Watch out for that outlier.

#What are the highest odometer readings?
usedcarworks_df.sort_values(by=['Kilometers_Driven'], ascending = False)

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
2328	BMW X5 xDrive 30d M Sport	Chennai	6500000	Diesel	Automatic	First	15.97 kmpl	2993 CC	258 bhp	5.00	55.07	65.00	BMW	3
340	Skoda Octavia Ambition Plus 2.0 TDI AT	Kolkata	775000	Diesel	Automatic	First	19.3 kmpl	1968 CC	141 bhp	5.00	25.83	7.50	Skoda	7
1860	Volkswagen Vento Diesel Highline	Chennai	720000	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	5.90	Volkswagen	7
358	Hyundai i10 Magna 1.2	Chennai	620000	Petrol	Manual	First	20.36 kmpl	1197 CC	78.9 bhp	5.00	10.25	2.70	Hyundai	11
2823	Volkswagen Jetta 2013-2015 2.0L TDI Highline AT	Chennai	480000	Diesel	Automatic	First	16.96 kmpl	1968 CC	138.03 bhp	5.00	10.15	13.00	Volkswagen	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5339	Hyundai i20 Active SX Dual Tone Petrol	Pune	1000	Petrol	Manual	First	17.19 kmpl	1197 CC	81.86 bhp	5.00	10.25	8.60	Hyundai	1
1598	Tata Xenon XT EX 4X2	Jaipur	1000	Diesel	Manual	First	13.49 kmpl	2179 CC	138.03 bhp	5.00	7.63	7.50	Tata	3
1242	Jaguar XE 2.0L Diesel Prestige	Delhi	1000	Diesel	Automatic	First	13.6 kmpl	1999 CC	177 bhp	5.00	52.77	40.00	Jaguar	2
5606	Maruti Wagon R ZXI AMT 1.2	Mumbai	600	Petrol	Automatic	First	21.5 kmpl	1197 CC	81.80 bhp	5.00	6.80	6.25	Maruti	1
1361	Maruti Alto 800 2016-2019 VXI	Mumbai	171	Petrol	Manual	First	24.7 kmpl	796 CC	47.3 bhp	5.00	4.36	3.60	Maruti	1

7253 rows × 14 columns

Can the BMW X5 xDrive 30d M Sport accumulated such large amount of miles?

Lets assume that the year is 2020. The car is 3 years old.

6500000/3 = 2'166,667 km per year or 5,936 per day. This is impossible!!! A car would have to travel at 247kmph continuously during 24 hours, for 3 years to achieve this.

If we assume that the year is 2022, and the car is 5 years old:

6500000/4 = 1'300,00 km per year or 3,561 km per day. This means that the car will have to travel at 148kmph continuously during 24 hours, for 5 years to achieve this. This is not feasible.

As a reference check this: https://www.guinnessworldrecords.com/world-records/longest-driven-journey

So we can drop this value.

#Lets drop that unusually high value
usedcarworks_df.drop(usedcarworks_df[usedcarworks_df["Kilometers_Driven"]==usedcarworks_df['Kilometers_Driven'].max()].index,inplace=True)
usedcarworks_df.sort_values(by=['Kilometers_Driven'], ascending = False)

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
340	Skoda Octavia Ambition Plus 2.0 TDI AT	Kolkata	775000	Diesel	Automatic	First	19.3 kmpl	1968 CC	141 bhp	5.00	25.83	7.50	Skoda	7
1860	Volkswagen Vento Diesel Highline	Chennai	720000	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	5.90	Volkswagen	7
358	Hyundai i10 Magna 1.2	Chennai	620000	Petrol	Manual	First	20.36 kmpl	1197 CC	78.9 bhp	5.00	10.25	2.70	Hyundai	11
2823	Volkswagen Jetta 2013-2015 2.0L TDI Highline AT	Chennai	480000	Diesel	Automatic	First	16.96 kmpl	1968 CC	138.03 bhp	5.00	10.15	13.00	Volkswagen	5
3092	Honda City i VTEC SV	Kolkata	480000	Petrol	Manual	First	17.4 kmpl	1497 CC	117.3 bhp	5.00	13.58	5.00	Honda	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2054	Maruti Ignis 1.2 Alpha	Mumbai	1000	Petrol	Manual	First	20.89 kmpl	1197 CC	81.80 bhp	5.00	7.87	6.36	Maruti	3
2646	Ford Figo 2015-2019 1.2P Sports Edition MT	Mumbai	1000	Petrol	Manual	First	18.12 kmpl	1196 CC	86.8 bhp	5.00	7.65	6.80	Ford	1
1598	Tata Xenon XT EX 4X2	Jaipur	1000	Diesel	Manual	First	13.49 kmpl	2179 CC	138.03 bhp	5.00	7.63	7.50	Tata	3
5606	Maruti Wagon R ZXI AMT 1.2	Mumbai	600	Petrol	Automatic	First	21.5 kmpl	1197 CC	81.80 bhp	5.00	6.80	6.25	Maruti	1
1361	Maruti Alto 800 2016-2019 VXI	Mumbai	171	Petrol	Manual	First	24.7 kmpl	796 CC	47.3 bhp	5.00	4.36	3.60	Maruti	1

7252 rows × 14 columns

That car that had the unusually high number of km was dropped.

#revisit the odometer
#lets see the distribution of the kilometers driven
plt.figure(figsize=(15,8))
sns.boxplot(x = usedcarworks_df["Kilometers_Driven"])
plt.title("Distribution of Odometer", fontsize=15)
plt.show()

usedcarworks_df.sort_values(by=['Kilometers_Driven'], ascending = False).head(20)

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
340	Skoda Octavia Ambition Plus 2.0 TDI AT	Kolkata	775000	Diesel	Automatic	First	19.3 kmpl	1968 CC	141 bhp	5.00	25.83	7.50	Skoda	7
1860	Volkswagen Vento Diesel Highline	Chennai	720000	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	5.90	Volkswagen	7
358	Hyundai i10 Magna 1.2	Chennai	620000	Petrol	Manual	First	20.36 kmpl	1197 CC	78.9 bhp	5.00	10.25	2.70	Hyundai	11
2823	Volkswagen Jetta 2013-2015 2.0L TDI Highline AT	Chennai	480000	Diesel	Automatic	First	16.96 kmpl	1968 CC	138.03 bhp	5.00	10.15	13.00	Volkswagen	5
3092	Honda City i VTEC SV	Kolkata	480000	Petrol	Manual	First	17.4 kmpl	1497 CC	117.3 bhp	5.00	13.58	5.00	Honda	5
4491	Hyundai i20 Magna Optional 1.2	Bangalore	445000	Petrol	Manual	First	18.5 kmpl	1197 CC	82.9 bhp	5.00	10.25	4.45	Hyundai	7
6921	Maruti Swift Dzire Tour LDI	Jaipur	350000	Diesel	Manual	First	23.4 kmpl	1248 CC	74 bhp	5.00	7.88	NaN	Maruti	8
3649	Tata Indigo LS	Jaipur	300000	Diesel	Manual	First	17.0 kmpl	1405 CC	70 bhp	5.00	7.63	1.00	Tata	12
1528	Toyota Innova 2.5 G (Diesel) 8 Seater BS IV	Hyderabad	299322	Diesel	Manual	First	12.8 kmpl	2494 CC	102 bhp	8.00	24.01	4.00	Toyota	15
6919	ISUZU D-MAX V-Cross 4X4	Jaipur	290000	Diesel	Manual	First	12.4 kmpl	2499 CC	134 bhp	5.00	33.68	NaN	ISUZU	3
1975	Skoda Superb 1.8 TSI MT	Pune	282000	Petrol	Manual	First	13.14 kmpl	1798 CC	160 bhp	5.00	33.36	3.30	Skoda	8
29	Toyota Innova 2.5 V Diesel 7-seater	Mumbai	262000	Diesel	Manual	Fourth & Above	12.8 kmpl	2494 CC	102 bhp	7.00	24.01	4.00	Toyota	13
1846	Toyota Innova 2.5 GX (Diesel) 7 Seater	Pune	255000	Diesel	Manual	Third	12.99 kmpl	2494 CC	100.6 bhp	7.00	24.01	6.50	Toyota	7
5834	Toyota Innova 2.5 G (Diesel) 8 Seater	Jaipur	250000	Diesel	Manual	Second	12.99 kmpl	2494 CC	100.6 bhp	8.00	24.01	7.90	Toyota	7
3894	Toyota Innova 2.5 GX 7 STR	Hyderabad	250000	Diesel	Manual	Second	12.8 kmpl	2494 CC	102 bhp	7.00	24.01	6.50	Toyota	10
4719	Toyota Innova 2.5 GX (Diesel) 8 Seater	Chennai	250000	Diesel	Manual	First	12.99 kmpl	2494 CC	100.6 bhp	8.00	24.01	10.90	Toyota	6
1101	Toyota Innova 2.5 V Diesel 7-seater	Chennai	248000	Diesel	Manual	Second	12.8 kmpl	2494 CC	102 bhp	7.00	24.01	9.20	Toyota	9
4792	Toyota Fortuner 3.0 Diesel	Chennai	242000	Diesel	Manual	First	11.5 kmpl	2982 CC	171 bhp	7.00	37.17	11.00	Toyota	10
2988	Toyota Innova 2.5 G4 Diesel 8-seater	Chennai	240000	Diesel	Manual	Fourth & Above	12.8 kmpl	2494 CC	102 bhp	8.00	24.01	5.10	Toyota	15
2346	Mahindra Xylo D2 BSIV	Chennai	234000	Diesel	Manual	Second	14.0 kmpl	2489 CC	95 bhp	8.00	11.67	3.50	Mahindra	7

Take a look at index 340 and 1860, surpassing 700,000 km. Is this possible? So index 340, with 775,000 km in 7 years represent rougly 111 km/year or 303 km/day. Can a car run 303 km/day? Yes, this is possible! Assume that every day, a car goes back and forth between city A and city B and the distance is 150km. The car goes at an avergae of speed of 50km/hr. So in average, the car can complete the trip in 3 hours one way or 6 hours round trip. So this is very possible.

After looking at all other high km cars, we conclude that those outliers are good data.

Fuel Type

Fue Type is a categorical value that contains the units of Fuel. There are no null values in this column.

#Fuel_type
#How many different types of Fuel_Type?
usedcarworks_df['Fuel_Type'].value_counts()

Diesel      3851
Petrol      3325
CNG           62
LPG           12
Electric       2
Name: Fuel_Type, dtype: int64

Lets take a look at every type of FUel Type.

#lets see the distribution of cars by fuel type
plt.figure(figsize=(15,8))
sns.histplot(data=usedcarworks_df,x='Fuel_Type')
plt.title("Distribution of Cars by Fuel Type", fontsize=15)
plt.show()

Most cars are Diesel and Petrol (Gasoline).

Diesel Cars

#Lets look at DIESEL cars
dieselcars_df = usedcarworks_df[usedcarworks_df['Fuel_Type']== 'Diesel']
dieselcars_df

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.00	16.06	12.50	Hyundai	5
3	Maruti Ertiga VDI	Chennai	87000	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.00	11.27	6.00	Maruti	8
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.00	53.14	17.74	Audi	7
6	Nissan Micra Diesel XV	Jaipur	86999	Diesel	Manual	First	23.08 kmpl	1461 CC	63.1 bhp	5.00	9.47	3.50	Nissan	7
7	Toyota Innova Crysta 2.8 GX AT 8S	Mumbai	36000	Diesel	Automatic	First	11.36 kmpl	2755 CC	171.5 bhp	8.00	21.00	17.50	Toyota	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7241	BMW 5 Series 525d Sedan	Delhi	80000	Diesel	Automatic	First	16.73 kmpl	2993 CC	218 bhp	5.00	67.87	NaN	BMW	8
7243	Renault Duster 85PS Diesel RxL	Chennai	70000	Diesel	Manual	First	19.87 kmpl	1461 CC	83.8 bhp	5.00	15.05	NaN	Renault	5
7248	Volkswagen Vento Diesel Trendline	Hyderabad	89411	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	NaN	Volkswagen	9
7250	Nissan Micra Diesel XV	Kolkata	28000	Diesel	Manual	First	23.08 kmpl	1461 CC	63.1 bhp	5.00	9.47	NaN	Nissan	8
7252	Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...	Kochi	72443	Diesel	Automatic	First	10.0 kmpl	2148 CC	170 bhp	5.00	86.97	NaN	Mercedes-Benz	6

3851 rows × 14 columns

not much to determine from this yet

#What are the most expensive Diesel
dieselcars_df.sort_values(by=['Price'], ascending = False).head(20)

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
4079	Land Rover Range Rover 3.0 Diesel LWB Vogue	Hyderabad	25000	Diesel	Automatic	First	13.33 kmpl	2993 CC	255 bhp	5.00	230.00	160.00	Land Rover	3
1505	Land Rover Range Rover Sport SE	Kochi	26013	Diesel	Automatic	First	12.65 kmpl	2993 CC	255 bhp	5.00	139.00	97.07	Land Rover	1
5535	BMW X6 xDrive 40d M Sport	Ahmedabad	97003	Diesel	Automatic	First	15.87 kmpl	2993 CC	308.43 bhp	5.00	55.07	85.00	BMW	5
2422	Jaguar XJ 3.0L Portfolio	Delhi	12000	Diesel	Automatic	First	10.5 kmpl	2993 CC	271.23 bhp	4.00	69.67	79.00	Jaguar	4
1885	Mercedes-Benz GLS 350d Grand Edition	Delhi	6000	Diesel	Automatic	First	11.0 kmpl	2987 CC	258 bhp	7.00	102.00	79.00	Mercedes-Benz	2
2319	Mercedes-Benz E-Class E 350 d	Coimbatore	9312	Diesel	Automatic	First	18.0 kmpl	2987 CC	258 bhp	5.00	92.50	78.80	Mercedes-Benz	1
3489	Porsche Panamera Diesel	Bangalore	19828	Diesel	Automatic	Second	17.85 kmpl	2967 CC	300 bhp	4.00	136.00	75.00	Porsche	7
2659	Porsche Panamera Diesel	Kolkata	25100	Diesel	Automatic	First	17.85 kmpl	2967 CC	300 bhp	4.00	136.00	75.00	Porsche	5
2907	Jaguar XJ 3.0L Portfolio	Hyderabad	26000	Diesel	Automatic	First	14.47 kmpl	2993 CC	301.73 bhp	5.00	69.67	75.00	Jaguar	5
1583	Audi Q7 45 TDI Quattro Premium Plus	Kochi	6773	Diesel	Automatic	First	14.75 kmpl	2967 CC	245 bhp	7.00	91.38	72.94	Audi	1
5603	Porsche Panamera 2010 2013 Diesel	Delhi	36400	Diesel	Automatic	First	7.5 kmpl	4806 CC	394.3 bhp	4.00	136.00	72.00	Porsche	7
1690	Mercedes-Benz GLE 350d	Coimbatore	40129	Diesel	Automatic	First	11.57 kmpl	2987 CC	254.79 bhp	5.00	95.13	70.80	Mercedes-Benz	2
3752	Land Rover Range Rover Sport HSE	Kochi	38467	Diesel	Automatic	First	12.65 kmpl	2993 CC	255 bhp	5.00	160.00	70.66	Land Rover	5
282	Audi Q7 3.0 TDI Quattro Technology	Kochi	11287	Diesel	Automatic	First	12.07 kmpl	2967 CC	241.4 bhp	7.00	101.96	70.36	Audi	2
2541	Mercedes-Benz E-Class 2015-2017 E350 CDI Avant...	Kochi	21603	Diesel	Automatic	First	13.0 kmpl	2987 CC	265 bhp	5.00	86.97	70.15	Mercedes-Benz	2
4614	BMW X5 X5 M	Chennai	15000	Diesel	Automatic	First	15.97 kmpl	2993 CC	258 bhp	5.00	55.07	70.00	BMW	3
1352	Land Rover Range Rover 3.6 TDV8 Vogue SE Diesel	Bangalore	66538	Diesel	Automatic	First	10.2 kmpl	4367 CC	308 bhp	7.00	120.00	70.00	Land Rover	8
1194	Mercedes-Benz E-Class 2015-2017 E350 CDI Avant...	Mumbai	15000	Diesel	Automatic	First	13.0 kmpl	2987 CC	265 bhp	5.00	86.97	69.50	Mercedes-Benz	3
4360	BMW 6 Series Gran Coupe	Kochi	20004	Diesel	Automatic	First	17.54 kmpl	2993 CC	313 bhp	4.00	55.07	68.94	BMW	2
5009	Audi Q7 45 TDI Quattro Technology	Pune	59500	Diesel	Automatic	First	14.75 kmpl	2967 CC	245 bhp	7.00	99.92	68.00	Audi	3

dieselcars_df.sort_values(by=['Kilometers_Driven'], ascending = False).head(20)

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
340	Skoda Octavia Ambition Plus 2.0 TDI AT	Kolkata	775000	Diesel	Automatic	First	19.3 kmpl	1968 CC	141 bhp	5.00	25.83	7.50	Skoda	7
1860	Volkswagen Vento Diesel Highline	Chennai	720000	Diesel	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	5.90	Volkswagen	7
2823	Volkswagen Jetta 2013-2015 2.0L TDI Highline AT	Chennai	480000	Diesel	Automatic	First	16.96 kmpl	1968 CC	138.03 bhp	5.00	10.15	13.00	Volkswagen	5
6921	Maruti Swift Dzire Tour LDI	Jaipur	350000	Diesel	Manual	First	23.4 kmpl	1248 CC	74 bhp	5.00	7.88	NaN	Maruti	8
3649	Tata Indigo LS	Jaipur	300000	Diesel	Manual	First	17.0 kmpl	1405 CC	70 bhp	5.00	7.63	1.00	Tata	12
1528	Toyota Innova 2.5 G (Diesel) 8 Seater BS IV	Hyderabad	299322	Diesel	Manual	First	12.8 kmpl	2494 CC	102 bhp	8.00	24.01	4.00	Toyota	15
6919	ISUZU D-MAX V-Cross 4X4	Jaipur	290000	Diesel	Manual	First	12.4 kmpl	2499 CC	134 bhp	5.00	33.68	NaN	ISUZU	3
29	Toyota Innova 2.5 V Diesel 7-seater	Mumbai	262000	Diesel	Manual	Fourth & Above	12.8 kmpl	2494 CC	102 bhp	7.00	24.01	4.00	Toyota	13
1846	Toyota Innova 2.5 GX (Diesel) 7 Seater	Pune	255000	Diesel	Manual	Third	12.99 kmpl	2494 CC	100.6 bhp	7.00	24.01	6.50	Toyota	7
4719	Toyota Innova 2.5 GX (Diesel) 8 Seater	Chennai	250000	Diesel	Manual	First	12.99 kmpl	2494 CC	100.6 bhp	8.00	24.01	10.90	Toyota	6
3894	Toyota Innova 2.5 GX 7 STR	Hyderabad	250000	Diesel	Manual	Second	12.8 kmpl	2494 CC	102 bhp	7.00	24.01	6.50	Toyota	10
5834	Toyota Innova 2.5 G (Diesel) 8 Seater	Jaipur	250000	Diesel	Manual	Second	12.99 kmpl	2494 CC	100.6 bhp	8.00	24.01	7.90	Toyota	7
1101	Toyota Innova 2.5 V Diesel 7-seater	Chennai	248000	Diesel	Manual	Second	12.8 kmpl	2494 CC	102 bhp	7.00	24.01	9.20	Toyota	9
4792	Toyota Fortuner 3.0 Diesel	Chennai	242000	Diesel	Manual	First	11.5 kmpl	2982 CC	171 bhp	7.00	37.17	11.00	Toyota	10
2988	Toyota Innova 2.5 G4 Diesel 8-seater	Chennai	240000	Diesel	Manual	Fourth & Above	12.8 kmpl	2494 CC	102 bhp	8.00	24.01	5.10	Toyota	15
2346	Mahindra Xylo D2 BSIV	Chennai	234000	Diesel	Manual	Second	14.0 kmpl	2489 CC	95 bhp	8.00	11.67	3.50	Mahindra	7
3713	Chevrolet Captiva LT	Jaipur	232000	Diesel	Manual	First	11.5 kmpl	1991 CC	147.9 bhp	7.00	11.30	3.50	Chevrolet	11
5647	Toyota Qualis Fleet A3	Mumbai	227000	Diesel	Manual	Fourth & Above	0.0 kmpl	2446 CC	NaN	8.00	24.01	2.20	Toyota	19
1068	Toyota Etios Liva GD	Pune	225000	Diesel	Manual	Second	23.59 kmpl	1364 CC	NaN	5.00	8.53	2.60	Toyota	8
181	Ford Fiesta 1.4 TDCi EXI Limited Edition	Chennai	216000	Diesel	Manual	Second	17.8 kmpl	1399 CC	68 bhp	5.00	11.69	1.45	Ford	14

Petrol Cars

#lets see the PETROL Cars
petrolcars_df = usedcarworks_df[usedcarworks_df['Fuel_Type']=='Petrol']
petrolcars_df

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
2	Honda Jazz V	Chennai	46000	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.00	8.61	4.50	Honda	9
10	Maruti Ciaz Zeta	Kochi	25692	Petrol	Manual	First	21.56 kmpl	1462 CC	103.25 bhp	5.00	10.65	9.95	Maruti	2
11	Honda City 1.5 V AT Sunroof	Kolkata	60000	Petrol	Automatic	First	16.8 kmpl	1497 CC	116.3 bhp	5.00	13.58	4.49	Honda	8
22	Audi A6 2011-2015 35 TFSI Technology	Mumbai	55985	Petrol	Automatic	First	13.53 kmpl	1984 CC	177.01 bhp	5.00	67.10	23.50	Audi	5
23	Hyundai i20 1.2 Magna	Kolkata	45807	Petrol	Manual	First	18.5 kmpl	1197 CC	80 bhp	5.00	10.25	1.87	Hyundai	10
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7245	Honda Amaze S i-Vtech	Kochi	44776	Petrol	Manual	First	18.0 kmpl	1198 CC	86.7 bhp	5.00	8.54	NaN	Honda	5
7246	Hyundai Grand i10 AT Asta	Coimbatore	18242	Petrol	Automatic	First	18.9 kmpl	1197 CC	82 bhp	5.00	7.45	NaN	Hyundai	4
7247	Hyundai EON D Lite Plus	Coimbatore	21190	Petrol	Manual	First	21.1 kmpl	814 CC	55.2 bhp	5.00	10.25	NaN	Hyundai	5
7249	Volkswagen Polo GT TSI	Mumbai	59000	Petrol	Automatic	First	17.21 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	5
7251	Volkswagen Polo GT TSI	Pune	52262	Petrol	Automatic	Third	17.2 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	7

3325 rows × 14 columns

The mileage range for petrol cars seems to be ok.

CNG Cars

#lets see the CNG
cngcars_df = usedcarworks_df[usedcarworks_df['Fuel_Type']=='CNG']
cngcars_df

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
0	Maruti Wagon R LXI CNG	Mumbai	72000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	1.75	Maruti	10
127	Maruti Wagon R LXI CNG	Pune	89900	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.25	Maruti	7
328	Maruti Zen Estilo LXI Green (CNG)	Pune	42496	CNG	Manual	First	26.3 km/kg	998 CC	67.1 bhp	5.00	7.88	1.40	Maruti	12
440	Maruti Eeco 5 STR With AC Plus HTR CNG	Kochi	31841	CNG	Manual	First	15.1 km/kg	1196 CC	73 bhp	5.00	4.92	4.70	Maruti	3
839	Maruti Alto Green LXi (CNG)	Delhi	65537	CNG	Manual	First	26.83 km/kg	796 CC	38.4 bhp	5.00	4.36	2.10	Maruti	8
893	Hyundai Accent Executive CNG	Hyderabad	95637	CNG	Manual	Second	13.2 km/kg	1495 CC	93.7 bhp	5.00	10.25	1.90	Hyundai	10
1135	Maruti Zen Estilo LXI Green (CNG)	Ahmedabad	76000	CNG	Manual	First	26.3 km/kg	998 CC	67.1 bhp	5.00	7.88	2.00	Maruti	9
1218	Maruti Wagon R LXI CNG	Pune	70000	CNG	Manual	First	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	3.65	Maruti	5
1269	Maruti Alto 800 2016-2019 CNG LXI	Mumbai	10600	CNG	Manual	First	33.44 km/kg	796 CC	40.3 bhp	4.00	4.36	3.50	Maruti	2
1323	Maruti Eeco 5 STR With AC Plus HTR CNG	Mumbai	20000	CNG	Manual	First	15.1 km/kg	1196 CC	73 bhp	5.00	4.92	4.50	Maruti	2
1332	Maruti Wagon R CNG LXI	Pune	79494	CNG	Manual	First	33.54 km/kg	998 CC	67.04 bhp	5.00	5.54	2.96	Maruti	7
1357	Maruti Wagon R LXI CNG	Mumbai	40000	CNG	Manual	Second	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.15	Maruti	6
1573	Maruti Ertiga VXI CNG	Mumbai	19234	CNG	Manual	First	17.5 km/kg	1373 CC	80.46 bhp	7.00	11.27	8.35	Maruti	3
1780	Maruti Wagon R LXI CNG	Mumbai	25755	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.15	Maruti	7
1911	Maruti Wagon R LXI CNG	Ahmedabad	77000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.30	Maruti	4
1914	Hyundai Xcent 1.2 Kappa S Option CNG	Chennai	46000	CNG	Manual	First	25.0 km/kg	1197 CC	81.86 bhp	5.00	8.23	4.95	Hyundai	3
2059	Maruti Wagon R CNG LXI	Mumbai	54000	CNG	Manual	First	33.54 km/kg	998 CC	67.04 bhp	5.00	5.58	3.05	Maruti	7
2110	Tata Indigo CS Emax CNG GLX	Pune	41000	CNG	Manual	First	24.6 km/kg	1193 CC	55.23 bhp	5.00	7.63	3.25	Tata	6
2228	Maruti Ertiga VXI CNG	Mumbai	65263	CNG	Manual	First	17.5 km/kg	1373 CC	80.46 bhp	7.00	11.27	7.50	Maruti	4
2323	Hyundai Xcent 1.2 Kappa S Option CNG	Delhi	36676	CNG	Manual	First	25.0 km/kg	1197 CC	81.86 bhp	5.00	8.23	5.00	Hyundai	4
2371	Maruti Wagon R CNG LXI	Pune	29202	CNG	Manual	First	33.54 km/kg	998 CC	67.04 bhp	5.00	5.54	3.90	Maruti	6
2376	Maruti Wagon R LXI CNG	Hyderabad	83875	CNG	Manual	Second	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	2.95	Maruti	7
2437	Tata Indigo CS Emax CNG GLX	Chennai	75000	CNG	Manual	First	24.6 km/kg	1193 CC	55.23 bhp	5.00	7.63	3.25	Tata	5
2516	Maruti SX4 Green Vxi (CNG)	Mumbai	53000	CNG	Manual	First	22.1 km/kg	1586 CC	85.7 bhp	5.00	7.88	3.26	Maruti	6
2527	Hyundai Santro Xing XO CNG	Pune	100000	CNG	Manual	Third	17.0 km/kg	1086 CC	NaN	5.00	4.55	1.20	Hyundai	15
2638	Maruti Wagon R LXI CNG	Pune	90400	CNG	Manual	First	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	2.90	Maruti	8
2701	Maruti Celerio CNG VXI MT	Kochi	70536	CNG	Manual	First	31.79 km/kg	998 CC	58.33 bhp	5.00	5.87	4.58	Maruti	4
3008	Maruti Wagon R LXI CNG	Delhi	48500	CNG	Manual	Second	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	2.85	Maruti	6
3031	Maruti Ertiga VXI CNG	Mumbai	53000	CNG	Manual	First	22.8 km/kg	1373 CC	80.9 bhp	7.00	11.27	5.80	Maruti	7
3097	Maruti Wagon R LXI CNG	Pune	92000	CNG	Manual	Second	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	3.00	Maruti	8
3119	Maruti Alto K10 LXI CNG Optional	Kochi	44202	CNG	Manual	First	32.26 km/kg	998 CC	58.2 bhp	4.00	4.66	4.00	Maruti	2
3129	Maruti Wagon R CNG LXI	Delhi	74663	CNG	Manual	First	33.54 km/kg	998 CC	67.04 bhp	5.00	5.35	3.25	Maruti	6
3180	Maruti Eeco CNG 5 Seater AC	Ahmedabad	47001	CNG	Manual	First	20.0 km/kg	1196 CC	63 bhp	5.00	5.36	3.72	Maruti	5
3295	Maruti Wagon R LXI CNG	Delhi	62000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	2.80	Maruti	7
3455	Maruti Wagon R LXI CNG	Mumbai	41000	CNG	Manual	First	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	3.50	Maruti	6
3511	Maruti Alto Green LXi (CNG)	Hyderabad	92000	CNG	Manual	Second	26.83 km/kg	796 CC	38.4 bhp	5.00	4.36	2.10	Maruti	8
3553	Maruti Alto 800 2016-2019 CNG LXI	Pune	18000	CNG	Manual	First	33.44 km/kg	796 CC	40.3 bhp	4.00	4.36	3.00	Maruti	5
3586	Maruti Eeco 5 STR With AC Plus HTR CNG	Delhi	52000	CNG	Manual	Second	15.1 km/kg	1196 CC	73 bhp	5.00	4.92	2.60	Maruti	7
3722	Maruti SX4 Green Vxi (CNG)	Kochi	61000	CNG	Manual	First	22.1 km/kg	1586 CC	85.7 bhp	5.00	7.88	3.89	Maruti	5
3869	Maruti Alto 800 2016-2019 CNG LXI	Delhi	65537	CNG	Manual	Second	33.44 km/kg	796 CC	40.3 bhp	4.00	4.36	2.10	Maruti	8
3901	Toyota Corolla Altis 1.8 G CNG	Mumbai	63000	CNG	Manual	Second	14.53 km/kg	1798 CC	138 bhp	5.00	21.09	3.35	Toyota	10
4098	Maruti SX4 Green Vxi (CNG)	Delhi	82069	CNG	Manual	First	21.4 km/kg	1586 CC	103.3 bhp	5.00	7.88	2.65	Maruti	8
4141	Maruti Wagon R CNG LXI	Mumbai	47200	CNG	Manual	First	33.54 km/kg	998 CC	67.04 bhp	5.00	5.58	2.85	Maruti	6
4411	Maruti Wagon R LXI CNG	Kochi	68365	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.77	Maruti	6
4603	Maruti Eeco 5 STR With AC Plus HTR CNG	Kochi	21112	CNG	Manual	First	15.1 km/kg	1196 CC	73 bhp	5.00	4.92	5.38	Maruti	1
4769	Maruti Alto 800 2016-2019 CNG LXI	Kochi	24310	CNG	Manual	First	33.44 km/kg	796 CC	40.3 bhp	4.00	4.36	3.70	Maruti	3
4920	Maruti Wagon R LXI CNG	Pune	58000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.00	Maruti	6
5004	Maruti Eeco 5 STR With AC Plus HTR CNG	Kochi	43912	CNG	Manual	First	15.1 km/kg	1196 CC	73 bhp	5.00	4.92	4.33	Maruti	2
5178	Maruti Celerio CNG VXI MT	Delhi	64687	CNG	Manual	First	31.79 km/kg	998 CC	58.33 bhp	5.00	5.87	3.80	Maruti	5
5655	Maruti Wagon R LXI CNG	Mumbai	22001	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.65	Maruti	5
5710	Maruti Alto 800 CNG LXI	Mumbai	26200	CNG	Manual	First	30.46 km/kg	796 CC	47.3 bhp	5.00	4.36	2.15	Maruti	7
5747	Maruti Wagon R LXI CNG	Delhi	62549	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	2.95	Maruti	6
5876	Maruti Ertiga VXI CNG	Mumbai	23700	CNG	Manual	First	17.5 km/kg	1373 CC	80.46 bhp	7.00	11.27	8.25	Maruti	3
5881	Maruti Wagon R LXI CNG	Pune	55000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	3.70	Maruti	5
5902	Maruti Wagon R LXI CNG	Mumbai	72000	CNG	Manual	First	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	2.25	Maruti	8
5935	Maruti Alto K10 LXI CNG	Pune	59525	CNG	Manual	Second	32.26 km/kg	998 CC	58.2 bhp	4.00	4.36	3.10	Maruti	5
6019	Maruti Alto K10 LXI CNG	Delhi	40929	CNG	Manual	First	32.26 km/kg	998 CC	58.2 bhp	4.00	4.36	NaN	Maruti	6
6387	Hyundai Santro Xing GLS CNG	Bangalore	43985	CNG	Manual	First	11.88 km/kg	1086 CC	62.1 bhp	5.00	4.55	NaN	Hyundai	9
6538	Maruti Alto K10 LXI CNG	Kochi	47490	CNG	Manual	First	32.26 km/kg	998 CC	58.2 bhp	4.00	4.36	NaN	Maruti	5
6788	Maruti Alto K10 LXI CNG Optional	Delhi	33000	CNG	Manual	First	32.26 km/kg	998 CC	58.2 bhp	4.00	4.66	NaN	Maruti	3
6926	Maruti Eeco 5 STR With AC Plus HTR CNG	Mumbai	15500	CNG	Manual	First	15.1 km/kg	1196 CC	73 bhp	5.00	4.92	NaN	Maruti	3
6958	Maruti Alto 800 CNG LXI	Kolkata	14311	CNG	Manual	First	30.46 km/kg	796 CC	47.3 bhp	5.00	4.36	NaN	Maruti	7

Not much to tell from this.

LPG Cars

#lets see the LPG
lpgcars_df = usedcarworks_df[usedcar_df['Fuel_Type']=='LPG']
lpgcars_df

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
5	Hyundai EON LPG Era Plus Option	Hyderabad	75000	LPG	Manual	First	21.1 km/kg	814 CC	55.2 bhp	5.00	10.25	2.35	Hyundai	8
936	Maruti Wagon R LXI LPG BSIV	Hyderabad	72000	LPG	Manual	First	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	2.85	Maruti	8
987	Maruti Wagon R LXI DUO BSIII	Mumbai	64226	LPG	Manual	First	17.3 km/kg	1061 CC	57.5 bhp	5.00	5.51	1.45	Maruti	12
2278	Hyundai Santro Xing GLS LPG	Pune	55000	LPG	Manual	Second	13.45 km/kg	1086 CC	62.1 bhp	5.00	4.55	1.65	Hyundai	10
2385	Hyundai Santro Xing GLS LPG	Hyderabad	17000	LPG	Manual	First	13.45 km/kg	1086 CC	62.1 bhp	5.00	4.55	3.10	Hyundai	9
2436	Hyundai i10 Magna LPG	Jaipur	53000	LPG	Manual	Second	19.2 km/kg	1086 CC	68.05 bhp	5.00	10.25	2.85	Hyundai	8
2941	Hyundai Santro Xing GL Plus LPG	Hyderabad	56000	LPG	Manual	First	13.45 km/kg	1086 CC	62.1 bhp	5.00	4.55	3.20	Hyundai	6
3595	Maruti Wagon R LX DUO BSIII	Hyderabad	65047	LPG	Manual	First	17.3 km/kg	1061 CC	57.5 bhp	5.00	5.51	1.95	Maruti	13
5506	Maruti Wagon R Duo LX BSIII	Hyderabad	72389	LPG	Manual	First	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	3.20	Maruti	8
5997	Maruti Wagon R Duo Lxi	Jaipur	70000	LPG	Manual	First	26.2 km/kg	998 CC	58.2 bhp	5.00	5.51	2.27	Maruti	10
6260	Maruti Wagon R LXI DUO BSIII	Jaipur	87829	LPG	Manual	First	17.3 km/kg	1061 CC	57.5 bhp	5.00	5.51	NaN	Maruti	11
6918	Hyundai Accent Executive LPG	Jaipur	40000	LPG	Manual	Second	13.2 km/kg	1495 CC	93.7 bhp	5.00	10.25	NaN	Hyundai	10

The mileage for LPG cars seems to be within acceptable ranges

Electric Cars

#lets revise the Electric Cars
electricars_df = usedcarworks_df[usedcarworks_df['Fuel_Type'] =='Electric']
electricars_df

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
4446	Mahindra E Verito D4	Chennai	50000	Electric	Automatic	First	NaN	72 CC	41 bhp	5.00	13.58	13.00	Mahindra	4
4904	Toyota Prius 2009-2016 Z4	Mumbai	44000	Electric	Automatic	First	NaN	1798 CC	73 bhp	5.00	24.01	12.75	Toyota	9

Electric cars do not show any Mileage. How should we handle these values?

Notice that 33.7 KwH is the equivalent to 1 US gallon of gasoline (3.78541 L)

Mahindra E Verito D4 Specifications: https://www.mahindraverito.com/everito/discover-the-new-verito.aspx

https://www.cseindia.org/content/downloadreports/10875

From here we gathered that the battery size is 21.2KW and the range is 110Km.

Toyota Prius Z4 https://www.fueleconomy.gov/feg/Find.do?action=sbs&id=30919 ~49mpg

batsize= 21.2
range = 110
gal_to_e = 33.7
liter_to_g = 3.78541

kple_mahindra =  ((gal_to_e * range)/batsize)/liter_to_g
mpge_prius = 49
kple_prius = mpge_prius*1.6/liter_to_g

print ("The Mahindra E Verito D4 has a kmpge of {:.2f} kmple".format(kple_mahindra))
print ("The Toyota Prius 2009-2016 Z4 has a kmpge of {:.2f} kmple".format(kple_prius))

mahindraD4 = usedcarworks_df.loc[usedcarworks_df.Name== 'Mahindra E Verito D4']
priusZ4 = usedcarworks_df.loc[usedcarworks_df.Name== 'Toyota Prius 2009-2016 Z4']

The Mahindra E Verito D4 has a kmpge of 46.19 kmple
The Toyota Prius 2009-2016 Z4 has a kmpge of 20.71 kmple

#lets substitute the NaN values for electric cars
usedcarworks_df.loc[usedcarworks_df['Name'] == 'Mahindra E Verito D4', 'Mileage'] = "{:.2f}".format(kple_mahindra)+' kmple'
usedcarworks_df.loc[usedcarworks_df['Name'] == 'Toyota Prius 2009-2016 Z4', 'Mileage'] = "{:.2f}".format(kple_prius)+ ' kmple'

#lets see if the changes were made
#lets revise the Electric Cars
electricars_df = usedcarworks_df[usedcar_df['Fuel_Type'] =='Electric']
electricars_df

	Name	Location	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age
4446	Mahindra E Verito D4	Chennai	50000	Electric	Automatic	First	46.19 kmple	72 CC	41 bhp	5.00	13.58	13.00	Mahindra	4
4904	Toyota Prius 2009-2016 Z4	Mumbai	44000	Electric	Automatic	First	20.71 kmple	1798 CC	73 bhp	5.00	24.01	12.75	Toyota	9

Fuel Type Dummies

#Convert Fuel Type to dummies, don't drop any
usedcarworks_df = pd.get_dummies(usedcarworks_df, columns=['Fuel_Type'])
usedcarworks_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol
0	Maruti Wagon R LXI CNG	Mumbai	72000	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0
2	Honda Jazz V	Chennai	46000	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1
3	Maruti Ertiga VDI	Chennai	87000	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7248	Volkswagen Vento Diesel Trendline	Hyderabad	89411	Manual	First	20.54 kmpl	1598 CC	103.6 bhp	5.00	13.23	NaN	Volkswagen	9	0	1	0	0	0
7249	Volkswagen Polo GT TSI	Mumbai	59000	Automatic	First	17.21 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	5	0	0	0	0	1
7250	Nissan Micra Diesel XV	Kolkata	28000	Manual	First	23.08 kmpl	1461 CC	63.1 bhp	5.00	9.47	NaN	Nissan	8	0	1	0	0	0
7251	Volkswagen Polo GT TSI	Pune	52262	Automatic	Third	17.2 kmpl	1197 CC	103.6 bhp	5.00	10.15	NaN	Volkswagen	7	0	0	0	0	1
7252	Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...	Kochi	72443	Automatic	First	10.0 kmpl	2148 CC	170 bhp	5.00	86.97	NaN	Mercedes-Benz	6	0	1	0	0	0

7252 rows × 18 columns

Mileage

Mileage contains a unit next o a number. Lets separate the text next to the mileage number and save it in another column

#split the number from the units
carmileage= usedcarworks_df['Mileage'].str.split(" ", n=1, expand=True)
carmileage.head()

	0	1
0	26.6	km/kg
1	19.67	kmpl
2	18.2	kmpl
3	20.77	kmpl
4	15.2	kmpl

#drop original column "Mileage"
usedcarworks_df.drop(['Mileage'], axis=1, inplace=True)

#create 2 separate columns, for "Mileage" and "Mileage units"
usedcarworks_df['Mileage']=carmileage[0]
usedcarworks_df['mileageunits']=carmileage[1]

#clear from memory unnecessary dataframe
del carmileage

#visualize the head
usedcarworks_df.head()

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	mileageunits
0	Maruti Wagon R LXI CNG	Mumbai	72000	Manual	First	998 CC	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	26.6	km/kg
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Manual	First	1582 CC	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	19.67	kmpl
2	Honda Jazz V	Chennai	46000	Manual	First	1199 CC	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	18.2	kmpl
3	Maruti Ertiga VDI	Chennai	87000	Manual	First	1248 CC	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	20.77	kmpl
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Automatic	Second	1968 CC	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	15.2	kmpl

#we can drop the mileage units.
usedcarworks_df.drop(['mileageunits'], axis=1, inplace=True)
usedcarworks_df.head()

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
0	Maruti Wagon R LXI CNG	Mumbai	72000	Manual	First	998 CC	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	26.6
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Manual	First	1582 CC	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	19.67
2	Honda Jazz V	Chennai	46000	Manual	First	1199 CC	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	18.2
3	Maruti Ertiga VDI	Chennai	87000	Manual	First	1248 CC	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	20.77
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Automatic	Second	1968 CC	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	15.2

We dropped the units from the Mileage.

#Convert Mileage to numeric
usedcarworks_df['Mileage'] = pd.factorize(usedcarworks_df['Mileage'])[0]

#See outliers in mileage?
#lets see the distribution of the kilometers driven
plt.figure(figsize=(15,8))
sns.boxplot(x = usedcarworks_df["Mileage"])
plt.title("Distribution of Mileage", fontsize=15)
plt.show()

#From the graph above we see cars that are have Mileage well over 300 km per unit
#see a list of the highest Mileage cars and see if its possible

mileage200plus = usedcarworks_df.loc[usedcarworks_df['Mileage']>200].sort_values(by=['Mileage'], ascending = False)

mileage200plus

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
6855	Renault Koleos 4X2 MT	Hyderabad	92448	Manual	First	1995 CC	147.9 bhp	5.00	15.05	NaN	Renault	6	0	1	0	0	0	439
6834	Hyundai Elantra SX AT	Coimbatore	25132	Automatic	First	1797 CC	147.4 bhp	5.00	21.77	NaN	Hyundai	3	0	0	0	0	1	438
7122	Hyundai Elantra SX AT	Bangalore	25000	Automatic	Second	1797 CC	147.4 bhp	5.00	21.77	NaN	Hyundai	4	0	0	0	0	1	438
6515	BMW 5 Series 530i Sport Line	Kolkata	30342	Automatic	First	1998 CC	252 bhp	5.00	59.12	NaN	BMW	9	0	0	0	0	1	437
6470	Isuzu MU 7 4x2 HIPACK	Kolkata	45000	Manual	First	2999 CC	161 bhp	7.00	33.68	NaN	Isuzu	4	0	1	0	0	0	436
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5811	Skoda Superb 1.8 TSI	Pune	35102	Automatic	First	1798 CC	161.6 bhp	5.00	33.36	5.50	Skoda	10	0	0	0	0	1	201
6703	Mercedes-Benz E-Class E 220 d	Delhi	15000	Automatic	First	1950 CC	191.34 bhp	5.00	86.97	NaN	Mercedes-Benz	3	0	0	0	0	1	201
1113	Mercedes-Benz E-Class E 220 d	Kochi	20446	Automatic	First	1950 CC	191.34 bhp	5.00	86.97	51.35	Mercedes-Benz	2	0	0	0	0	1	201
3020	Hyundai Tucson CRDi	Delhi	80000	Manual	Second	1991 CC	112 bhp	7.00	27.82	1.75	Hyundai	14	0	1	0	0	0	201
473	Mercedes-Benz E-Class E 220 d	Kochi	26405	Automatic	First	1950 CC	191.34 bhp	5.00	86.97	51.43	Mercedes-Benz	2	0	0	0	0	1	201

1385 rows × 18 columns

We see Mileage in the high realms of 439 the first one being a Renault Koleos 4x2 MT.

Engine

The Engine column contains a unit embedded with a nymber.

usedcarworks_df['Engine']

0        998 CC
1       1582 CC
2       1199 CC
3       1248 CC
4       1968 CC
         ...   
7248    1598 CC
7249    1197 CC
7250    1461 CC
7251    1197 CC
7252    2148 CC
Name: Engine, Length: 7252, dtype: object

#how many 'Engine' rows are NaN
usedcarworks_df['Engine'].isna().value_counts()

False    7206
True       46
Name: Engine, dtype: int64

#find all Engines that are nan
usedcarworks_df.loc[usedcarworks_df['Engine'].isna()]

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	NaN	NaN	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	NaN	NaN	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	NaN	NaN	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	NaN	NaN	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	NaN	NaN	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	NaN	NaN	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	NaN	NaN	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14
1460	Land Rover Range Rover Sport 2005 2012 Sport	Coimbatore	69078	Manual	First	NaN	NaN	NaN	120.00	40.88	Land Rover	12	0	0	0	0	1	14
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	NaN	NaN	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133
2096	Hyundai Santro LP zipPlus	Coimbatore	52146	Manual	First	NaN	NaN	NaN	4.55	1.93	Hyundai	16	0	0	0	0	1	14
2264	Toyota Etios Liva V	Pune	24500	Manual	Second	NaN	NaN	NaN	8.53	2.95	Toyota	8	0	0	0	0	1	249
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	NaN	NaN	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	NaN	NaN	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133
2530	BMW 5 Series 520d Sedan	Kochi	64158	Automatic	First	NaN	NaN	NaN	67.87	17.89	BMW	6	0	1	0	0	0	163
2542	Hyundai Santro GLS II - Euro II	Bangalore	65000	Manual	Second	NaN	NaN	NaN	4.55	3.15	Hyundai	9	0	0	0	0	1	14
2623	BMW 5 Series 520d Sedan	Pune	95000	Automatic	Second	NaN	NaN	NaN	67.87	18.00	BMW	8	0	1	0	0	0	163
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	NaN	NaN	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133
2737	Maruti Wagon R Vx	Jaipur	200000	Manual	First	NaN	NaN	NaN	5.51	0.70	Maruti	19	0	0	0	0	1	188
2780	Hyundai Santro GLS II - Euro II	Pune	100000	Manual	First	NaN	NaN	NaN	4.55	1.60	Hyundai	11	0	0	0	0	1	14
2842	Hyundai Santro GLS II - Euro II	Bangalore	43000	Manual	First	NaN	NaN	NaN	4.55	3.25	Hyundai	8	0	0	0	0	1	14
3272	BMW 5 Series 520d Sedan	Mumbai	81000	Automatic	Second	NaN	NaN	NaN	67.87	10.50	BMW	12	0	1	0	0	0	163
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	NaN	NaN	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133
3520	BMW 5 Series 520d Sedan	Delhi	90000	Automatic	First	NaN	NaN	NaN	67.87	14.50	BMW	8	0	1	0	0	0	163
3522	Hyundai Santro GLS II - Euro II	Kochi	66400	Manual	First	NaN	NaN	NaN	4.55	2.66	Hyundai	8	0	0	0	0	1	14
3810	Honda CR-V AT With Sun Roof	Kolkata	27000	Automatic	First	NaN	NaN	NaN	33.51	11.99	Honda	7	0	0	0	0	1	33
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	NaN	NaN	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	NaN	NaN	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14
4229	Hyundai Santro Xing XG	Bangalore	79000	Manual	Second	NaN	NaN	NaN	4.55	1.65	Hyundai	15	0	0	0	0	1	72
4577	BMW 5 Series 520d Sedan	Delhi	72000	Automatic	Third	NaN	NaN	NaN	67.87	13.85	BMW	8	0	1	0	0	0	163
4604	Honda Jazz Select Edition	Pune	98000	Manual	First	NaN	NaN	NaN	9.60	3.15	Honda	9	0	0	0	0	1	378
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	NaN	NaN	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328
4712	Hyundai Santro Xing XG	Pune	80000	Manual	Second	NaN	NaN	NaN	4.55	0.90	Hyundai	17	0	0	0	0	1	72
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	NaN	NaN	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	NaN	NaN	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	NaN	NaN	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	NaN	NaN	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	NaN	NaN	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26
6541	Toyota Etios Liva Diesel TRD Sportivo	Bangalore	56600	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	1	0	0	0	132
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	NaN	NaN	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89
6633	Mahindra TUV 300 P4	Kolkata	27000	Manual	First	NaN	NaN	NaN	18.86	NaN	Mahindra	4	0	1	0	0	0	14
6643	BMW 5 Series 520d Sedan	Bangalore	150000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	NaN	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	NaN	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	NaN	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133
6880	BMW 5 Series 520d Sedan	Chennai	95000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163
6902	Toyota Etios Liva V	Kochi	59311	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	0	0	0	1	249

Notice that if Engine is nan, so is Power and number of seats. we may be able to obtain the mileage from the Name of the car.

#how many types of engines
carengine= usedcarworks_df['Engine'].str.split(" ", n=1, expand=True)
carengine[1].value_counts()

CC    7206
Name: 1, dtype: int64

All engines are CC. So it make sense to assume that all engines are CC.

#Engine contains text "CC" next to the actual engine block size, drop CC
def engine_to_num(Engine):
    """This function takes in a string representing an engine size in CC
    and converts it to a number. For example, '1000CC' becomes 1000.
    If the input is already numeric, which probably means it's NaN,
    this function just returns np.nan."""
    if isinstance(Engine, str):  # checks if `income_val` is a string
      return float(Engine.replace('CC', ''))
    else:  # this happens when the current income is np.nan
        return np.nan

col_transforms = {
    'Engine': engine_to_num
}

for k,v in col_transforms.items():
  usedcarworks_df[k] = usedcarworks_df[k].map(v)

usedcarworks_df.head(10)

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
0	Maruti Wagon R LXI CNG	Mumbai	72000	Manual	First	998.00	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Manual	First	1582.00	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1
2	Honda Jazz V	Chennai	46000	Manual	First	1199.00	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2
3	Maruti Ertiga VDI	Chennai	87000	Manual	First	1248.00	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Automatic	Second	1968.00	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4
5	Hyundai EON LPG Era Plus Option	Hyderabad	75000	Manual	First	814.00	55.2 bhp	5.00	10.25	2.35	Hyundai	8	0	0	0	1	0	5
6	Nissan Micra Diesel XV	Jaipur	86999	Manual	First	1461.00	63.1 bhp	5.00	9.47	3.50	Nissan	7	0	1	0	0	0	6
7	Toyota Innova Crysta 2.8 GX AT 8S	Mumbai	36000	Automatic	First	2755.00	171.5 bhp	8.00	21.00	17.50	Toyota	4	0	1	0	0	0	7
8	Volkswagen Vento Diesel Comfortline	Pune	64430	Manual	First	1598.00	103.6 bhp	5.00	13.23	5.20	Volkswagen	7	0	1	0	0	0	8
9	Tata Indica Vista Quadrajet LS	Chennai	65932	Manual	Second	1248.00	74 bhp	5.00	7.63	1.95	Tata	8	0	1	0	0	0	9

We still must do something about the Engine nan.

Notice that some car names contain the engine size

#Extract the engine size from the Name of the car
engineName_df= pd.DataFrame(usedcarworks_df.loc[usedcarworks_df['Engine'].isna()])
engineName_df['hasEngineinName'] = usedcarworks_df['Name'].str.extract('(\d.\d)', expand=True)
engineName_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	hasEngineinName
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	NaN	NaN	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14	1.5
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	NaN	NaN	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133	1.3
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	NaN	NaN	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133	1.3
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	NaN	NaN	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14	3.0
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	NaN	NaN	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27	1.3
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	NaN	NaN	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133	1.3
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	NaN	NaN	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14	1.5
1460	Land Rover Range Rover Sport 2005 2012 Sport	Coimbatore	69078	Manual	First	NaN	NaN	NaN	120.00	40.88	Land Rover	12	0	0	0	0	1	14	200
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	NaN	NaN	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133	1.3
2096	Hyundai Santro LP zipPlus	Coimbatore	52146	Manual	First	NaN	NaN	NaN	4.55	1.93	Hyundai	16	0	0	0	0	1	14	NaN
2264	Toyota Etios Liva V	Pune	24500	Manual	Second	NaN	NaN	NaN	8.53	2.95	Toyota	8	0	0	0	0	1	249	NaN
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	NaN	NaN	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133	1.3
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	NaN	NaN	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133	1.3
2530	BMW 5 Series 520d Sedan	Kochi	64158	Automatic	First	NaN	NaN	NaN	67.87	17.89	BMW	6	0	1	0	0	0	163	520
2542	Hyundai Santro GLS II - Euro II	Bangalore	65000	Manual	Second	NaN	NaN	NaN	4.55	3.15	Hyundai	9	0	0	0	0	1	14	NaN
2623	BMW 5 Series 520d Sedan	Pune	95000	Automatic	Second	NaN	NaN	NaN	67.87	18.00	BMW	8	0	1	0	0	0	163	520
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	NaN	NaN	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133	1.3
2737	Maruti Wagon R Vx	Jaipur	200000	Manual	First	NaN	NaN	NaN	5.51	0.70	Maruti	19	0	0	0	0	1	188	NaN
2780	Hyundai Santro GLS II - Euro II	Pune	100000	Manual	First	NaN	NaN	NaN	4.55	1.60	Hyundai	11	0	0	0	0	1	14	NaN
2842	Hyundai Santro GLS II - Euro II	Bangalore	43000	Manual	First	NaN	NaN	NaN	4.55	3.25	Hyundai	8	0	0	0	0	1	14	NaN
3272	BMW 5 Series 520d Sedan	Mumbai	81000	Automatic	Second	NaN	NaN	NaN	67.87	10.50	BMW	12	0	1	0	0	0	163	520
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	NaN	NaN	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133	1.3
3520	BMW 5 Series 520d Sedan	Delhi	90000	Automatic	First	NaN	NaN	NaN	67.87	14.50	BMW	8	0	1	0	0	0	163	520
3522	Hyundai Santro GLS II - Euro II	Kochi	66400	Manual	First	NaN	NaN	NaN	4.55	2.66	Hyundai	8	0	0	0	0	1	14	NaN
3810	Honda CR-V AT With Sun Roof	Kolkata	27000	Automatic	First	NaN	NaN	NaN	33.51	11.99	Honda	7	0	0	0	0	1	33	NaN
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	NaN	NaN	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285	1.3
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	NaN	NaN	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14	3.0
4229	Hyundai Santro Xing XG	Bangalore	79000	Manual	Second	NaN	NaN	NaN	4.55	1.65	Hyundai	15	0	0	0	0	1	72	NaN
4577	BMW 5 Series 520d Sedan	Delhi	72000	Automatic	Third	NaN	NaN	NaN	67.87	13.85	BMW	8	0	1	0	0	0	163	520
4604	Honda Jazz Select Edition	Pune	98000	Manual	First	NaN	NaN	NaN	9.60	3.15	Honda	9	0	0	0	0	1	378	NaN
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	NaN	NaN	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328	1.2
4712	Hyundai Santro Xing XG	Pune	80000	Manual	Second	NaN	NaN	NaN	4.55	0.90	Hyundai	17	0	0	0	0	1	72	NaN
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	NaN	NaN	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209	1.4
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	NaN	NaN	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133	1.3
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	NaN	NaN	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133	1.3
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	NaN	NaN	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14	1.5
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	NaN	NaN	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26	1.8
6541	Toyota Etios Liva Diesel TRD Sportivo	Bangalore	56600	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	1	0	0	0	132	NaN
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	NaN	NaN	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89	1.4
6633	Mahindra TUV 300 P4	Kolkata	27000	Manual	First	NaN	NaN	NaN	18.86	NaN	Mahindra	4	0	1	0	0	0	14	300
6643	BMW 5 Series 520d Sedan	Bangalore	150000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163	520
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	NaN	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133	1.3
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	NaN	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209	1.4
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	NaN	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133	1.3
6880	BMW 5 Series 520d Sedan	Chennai	95000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163	520
6902	Toyota Etios Liva V	Kochi	59311	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	0	0	0	1	249	NaN

#Only keep values that are smaller than a safe value for engine size like 10. So an extracted value like 300 is not Engine size, so ignore it.
engineName_df.loc[engineName_df['hasEngineinName'].astype(float) < 10,'EngineSize'] = 'True'
engineName_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	hasEngineinName	EngineSize
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	NaN	NaN	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14	1.5	True
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	NaN	NaN	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133	1.3	True
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	NaN	NaN	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133	1.3	True
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	NaN	NaN	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14	3.0	True
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	NaN	NaN	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27	1.3	True
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	NaN	NaN	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133	1.3	True
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	NaN	NaN	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14	1.5	True
1460	Land Rover Range Rover Sport 2005 2012 Sport	Coimbatore	69078	Manual	First	NaN	NaN	NaN	120.00	40.88	Land Rover	12	0	0	0	0	1	14	200	NaN
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	NaN	NaN	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133	1.3	True
2096	Hyundai Santro LP zipPlus	Coimbatore	52146	Manual	First	NaN	NaN	NaN	4.55	1.93	Hyundai	16	0	0	0	0	1	14	NaN	NaN
2264	Toyota Etios Liva V	Pune	24500	Manual	Second	NaN	NaN	NaN	8.53	2.95	Toyota	8	0	0	0	0	1	249	NaN	NaN
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	NaN	NaN	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133	1.3	True
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	NaN	NaN	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133	1.3	True
2530	BMW 5 Series 520d Sedan	Kochi	64158	Automatic	First	NaN	NaN	NaN	67.87	17.89	BMW	6	0	1	0	0	0	163	520	NaN
2542	Hyundai Santro GLS II - Euro II	Bangalore	65000	Manual	Second	NaN	NaN	NaN	4.55	3.15	Hyundai	9	0	0	0	0	1	14	NaN	NaN
2623	BMW 5 Series 520d Sedan	Pune	95000	Automatic	Second	NaN	NaN	NaN	67.87	18.00	BMW	8	0	1	0	0	0	163	520	NaN
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	NaN	NaN	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133	1.3	True
2737	Maruti Wagon R Vx	Jaipur	200000	Manual	First	NaN	NaN	NaN	5.51	0.70	Maruti	19	0	0	0	0	1	188	NaN	NaN
2780	Hyundai Santro GLS II - Euro II	Pune	100000	Manual	First	NaN	NaN	NaN	4.55	1.60	Hyundai	11	0	0	0	0	1	14	NaN	NaN
2842	Hyundai Santro GLS II - Euro II	Bangalore	43000	Manual	First	NaN	NaN	NaN	4.55	3.25	Hyundai	8	0	0	0	0	1	14	NaN	NaN
3272	BMW 5 Series 520d Sedan	Mumbai	81000	Automatic	Second	NaN	NaN	NaN	67.87	10.50	BMW	12	0	1	0	0	0	163	520	NaN
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	NaN	NaN	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133	1.3	True
3520	BMW 5 Series 520d Sedan	Delhi	90000	Automatic	First	NaN	NaN	NaN	67.87	14.50	BMW	8	0	1	0	0	0	163	520	NaN
3522	Hyundai Santro GLS II - Euro II	Kochi	66400	Manual	First	NaN	NaN	NaN	4.55	2.66	Hyundai	8	0	0	0	0	1	14	NaN	NaN
3810	Honda CR-V AT With Sun Roof	Kolkata	27000	Automatic	First	NaN	NaN	NaN	33.51	11.99	Honda	7	0	0	0	0	1	33	NaN	NaN
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	NaN	NaN	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285	1.3	True
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	NaN	NaN	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14	3.0	True
4229	Hyundai Santro Xing XG	Bangalore	79000	Manual	Second	NaN	NaN	NaN	4.55	1.65	Hyundai	15	0	0	0	0	1	72	NaN	NaN
4577	BMW 5 Series 520d Sedan	Delhi	72000	Automatic	Third	NaN	NaN	NaN	67.87	13.85	BMW	8	0	1	0	0	0	163	520	NaN
4604	Honda Jazz Select Edition	Pune	98000	Manual	First	NaN	NaN	NaN	9.60	3.15	Honda	9	0	0	0	0	1	378	NaN	NaN
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	NaN	NaN	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328	1.2	True
4712	Hyundai Santro Xing XG	Pune	80000	Manual	Second	NaN	NaN	NaN	4.55	0.90	Hyundai	17	0	0	0	0	1	72	NaN	NaN
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	NaN	NaN	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209	1.4	True
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	NaN	NaN	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133	1.3	True
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	NaN	NaN	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133	1.3	True
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	NaN	NaN	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14	1.5	True
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	NaN	NaN	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26	1.8	True
6541	Toyota Etios Liva Diesel TRD Sportivo	Bangalore	56600	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	1	0	0	0	132	NaN	NaN
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	NaN	NaN	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89	1.4	True
6633	Mahindra TUV 300 P4	Kolkata	27000	Manual	First	NaN	NaN	NaN	18.86	NaN	Mahindra	4	0	1	0	0	0	14	300	NaN
6643	BMW 5 Series 520d Sedan	Bangalore	150000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163	520	NaN
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	NaN	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133	1.3	True
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	NaN	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209	1.4	True
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	NaN	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133	1.3	True
6880	BMW 5 Series 520d Sedan	Chennai	95000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163	520	NaN
6902	Toyota Etios Liva V	Kochi	59311	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	0	0	0	1	249	NaN	NaN

#Add a temporary field True if the enginesize is valid
engineName_df.loc[engineName_df['EngineSize'] == "True",'Engine'] = engineName_df['hasEngineinName'].astype(float)*1000
engineName_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	hasEngineinName	EngineSize
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	1500.00	NaN	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14	1.5	True
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	1300.00	NaN	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133	1.3	True
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	1300.00	NaN	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133	1.3	True
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	3000.00	NaN	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14	3.0	True
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	1300.00	NaN	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27	1.3	True
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	1300.00	NaN	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133	1.3	True
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	1500.00	NaN	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14	1.5	True
1460	Land Rover Range Rover Sport 2005 2012 Sport	Coimbatore	69078	Manual	First	NaN	NaN	NaN	120.00	40.88	Land Rover	12	0	0	0	0	1	14	200	NaN
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	1300.00	NaN	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133	1.3	True
2096	Hyundai Santro LP zipPlus	Coimbatore	52146	Manual	First	NaN	NaN	NaN	4.55	1.93	Hyundai	16	0	0	0	0	1	14	NaN	NaN
2264	Toyota Etios Liva V	Pune	24500	Manual	Second	NaN	NaN	NaN	8.53	2.95	Toyota	8	0	0	0	0	1	249	NaN	NaN
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	1300.00	NaN	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133	1.3	True
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	1300.00	NaN	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133	1.3	True
2530	BMW 5 Series 520d Sedan	Kochi	64158	Automatic	First	NaN	NaN	NaN	67.87	17.89	BMW	6	0	1	0	0	0	163	520	NaN
2542	Hyundai Santro GLS II - Euro II	Bangalore	65000	Manual	Second	NaN	NaN	NaN	4.55	3.15	Hyundai	9	0	0	0	0	1	14	NaN	NaN
2623	BMW 5 Series 520d Sedan	Pune	95000	Automatic	Second	NaN	NaN	NaN	67.87	18.00	BMW	8	0	1	0	0	0	163	520	NaN
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	1300.00	NaN	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133	1.3	True
2737	Maruti Wagon R Vx	Jaipur	200000	Manual	First	NaN	NaN	NaN	5.51	0.70	Maruti	19	0	0	0	0	1	188	NaN	NaN
2780	Hyundai Santro GLS II - Euro II	Pune	100000	Manual	First	NaN	NaN	NaN	4.55	1.60	Hyundai	11	0	0	0	0	1	14	NaN	NaN
2842	Hyundai Santro GLS II - Euro II	Bangalore	43000	Manual	First	NaN	NaN	NaN	4.55	3.25	Hyundai	8	0	0	0	0	1	14	NaN	NaN
3272	BMW 5 Series 520d Sedan	Mumbai	81000	Automatic	Second	NaN	NaN	NaN	67.87	10.50	BMW	12	0	1	0	0	0	163	520	NaN
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	1300.00	NaN	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133	1.3	True
3520	BMW 5 Series 520d Sedan	Delhi	90000	Automatic	First	NaN	NaN	NaN	67.87	14.50	BMW	8	0	1	0	0	0	163	520	NaN
3522	Hyundai Santro GLS II - Euro II	Kochi	66400	Manual	First	NaN	NaN	NaN	4.55	2.66	Hyundai	8	0	0	0	0	1	14	NaN	NaN
3810	Honda CR-V AT With Sun Roof	Kolkata	27000	Automatic	First	NaN	NaN	NaN	33.51	11.99	Honda	7	0	0	0	0	1	33	NaN	NaN
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	1300.00	NaN	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285	1.3	True
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	3000.00	NaN	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14	3.0	True
4229	Hyundai Santro Xing XG	Bangalore	79000	Manual	Second	NaN	NaN	NaN	4.55	1.65	Hyundai	15	0	0	0	0	1	72	NaN	NaN
4577	BMW 5 Series 520d Sedan	Delhi	72000	Automatic	Third	NaN	NaN	NaN	67.87	13.85	BMW	8	0	1	0	0	0	163	520	NaN
4604	Honda Jazz Select Edition	Pune	98000	Manual	First	NaN	NaN	NaN	9.60	3.15	Honda	9	0	0	0	0	1	378	NaN	NaN
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	1200.00	NaN	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328	1.2	True
4712	Hyundai Santro Xing XG	Pune	80000	Manual	Second	NaN	NaN	NaN	4.55	0.90	Hyundai	17	0	0	0	0	1	72	NaN	NaN
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	1400.00	NaN	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209	1.4	True
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	1300.00	NaN	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133	1.3	True
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	1300.00	NaN	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133	1.3	True
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	1500.00	NaN	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14	1.5	True
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	1800.00	NaN	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26	1.8	True
6541	Toyota Etios Liva Diesel TRD Sportivo	Bangalore	56600	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	1	0	0	0	132	NaN	NaN
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	1400.00	NaN	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89	1.4	True
6633	Mahindra TUV 300 P4	Kolkata	27000	Manual	First	NaN	NaN	NaN	18.86	NaN	Mahindra	4	0	1	0	0	0	14	300	NaN
6643	BMW 5 Series 520d Sedan	Bangalore	150000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163	520	NaN
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	1300.00	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133	1.3	True
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	1400.00	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209	1.4	True
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	1300.00	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133	1.3	True
6880	BMW 5 Series 520d Sedan	Chennai	95000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163	520	NaN
6902	Toyota Etios Liva V	Kochi	59311	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	0	0	0	1	249	NaN	NaN

#drop unneeded columns, that is those that don't have a temporary value of True
engineName_df.drop(['EngineSize','hasEngineinName'], axis=1, inplace=True)

engineName_df.loc[engineName_df['Engine'].isna()]

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
1460	Land Rover Range Rover Sport 2005 2012 Sport	Coimbatore	69078	Manual	First	NaN	NaN	NaN	120.00	40.88	Land Rover	12	0	0	0	0	1	14
2096	Hyundai Santro LP zipPlus	Coimbatore	52146	Manual	First	NaN	NaN	NaN	4.55	1.93	Hyundai	16	0	0	0	0	1	14
2264	Toyota Etios Liva V	Pune	24500	Manual	Second	NaN	NaN	NaN	8.53	2.95	Toyota	8	0	0	0	0	1	249
2530	BMW 5 Series 520d Sedan	Kochi	64158	Automatic	First	NaN	NaN	NaN	67.87	17.89	BMW	6	0	1	0	0	0	163
2542	Hyundai Santro GLS II - Euro II	Bangalore	65000	Manual	Second	NaN	NaN	NaN	4.55	3.15	Hyundai	9	0	0	0	0	1	14
2623	BMW 5 Series 520d Sedan	Pune	95000	Automatic	Second	NaN	NaN	NaN	67.87	18.00	BMW	8	0	1	0	0	0	163
2737	Maruti Wagon R Vx	Jaipur	200000	Manual	First	NaN	NaN	NaN	5.51	0.70	Maruti	19	0	0	0	0	1	188
2780	Hyundai Santro GLS II - Euro II	Pune	100000	Manual	First	NaN	NaN	NaN	4.55	1.60	Hyundai	11	0	0	0	0	1	14
2842	Hyundai Santro GLS II - Euro II	Bangalore	43000	Manual	First	NaN	NaN	NaN	4.55	3.25	Hyundai	8	0	0	0	0	1	14
3272	BMW 5 Series 520d Sedan	Mumbai	81000	Automatic	Second	NaN	NaN	NaN	67.87	10.50	BMW	12	0	1	0	0	0	163
3520	BMW 5 Series 520d Sedan	Delhi	90000	Automatic	First	NaN	NaN	NaN	67.87	14.50	BMW	8	0	1	0	0	0	163
3522	Hyundai Santro GLS II - Euro II	Kochi	66400	Manual	First	NaN	NaN	NaN	4.55	2.66	Hyundai	8	0	0	0	0	1	14
3810	Honda CR-V AT With Sun Roof	Kolkata	27000	Automatic	First	NaN	NaN	NaN	33.51	11.99	Honda	7	0	0	0	0	1	33
4229	Hyundai Santro Xing XG	Bangalore	79000	Manual	Second	NaN	NaN	NaN	4.55	1.65	Hyundai	15	0	0	0	0	1	72
4577	BMW 5 Series 520d Sedan	Delhi	72000	Automatic	Third	NaN	NaN	NaN	67.87	13.85	BMW	8	0	1	0	0	0	163
4604	Honda Jazz Select Edition	Pune	98000	Manual	First	NaN	NaN	NaN	9.60	3.15	Honda	9	0	0	0	0	1	378
4712	Hyundai Santro Xing XG	Pune	80000	Manual	Second	NaN	NaN	NaN	4.55	0.90	Hyundai	17	0	0	0	0	1	72
6541	Toyota Etios Liva Diesel TRD Sportivo	Bangalore	56600	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	1	0	0	0	132
6633	Mahindra TUV 300 P4	Kolkata	27000	Manual	First	NaN	NaN	NaN	18.86	NaN	Mahindra	4	0	1	0	0	0	14
6643	BMW 5 Series 520d Sedan	Bangalore	150000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163
6880	BMW 5 Series 520d Sedan	Chennai	95000	Automatic	Second	NaN	NaN	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163
6902	Toyota Etios Liva V	Kochi	59311	Manual	First	NaN	NaN	NaN	8.53	NaN	Toyota	8	0	0	0	0	1	249

Now lets deal with special cases

Case: BMW 5 Series 520d Sedan 1995cc 188bhp

carname = "BMW 5 Series 520d Sedan"
engsize = 1995
enginebhp=188
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case LandRover Range Rover Sport 2005-2012 engine size: 4.4L V8 296 hp

carname = "Land Rover Range Rover Sport 2005 2012 Sport"
engsize= 4400
enginebhp=296
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case: Toyota Etios Liva V, 1197cc, 79bhp

carname='Toyota Etios Liva V'
enginesize=1197
enginebhp=79
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Hyundai Santro GLS II - Euro II 999 cc, 62bhp

carname='Hyundai Santro GLS II - Euro II'
enginesize=999
enginebhp=62
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Hyundai Santro LP zipPlus 999 cc, 62bhp

carname ='Hyundai Santro LP zipPlus'
enginesize=999
enginebhp=62
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Maruti Wagon R Vx 998cc, 65.71bhp

carname = 'Maruti Wagon R Vx'
enginesize=998
enginebhp=65.71
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Honda CR-V AT With Sun Roof 2400cc, 185bhp

carname= 'Honda CR-V AT With Sun Roof'
enginesize=2400
enginebhp=185
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Hyundai Santro Xing XG 1086cc, 63Bhp

carname= 'Hyundai Santro Xing XG'
enginesize=1086
enginebhp=63
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Honda Jazz Select Edition 1198cc, 89bhp

carname='Honda Jazz Select Edition'
enginesize=1198
enginebhp=89
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Toyota Etios Liva Diesel TRD Sportivo 1364cc, 67bhp

carname= 'Toyota Etios Liva Diesel TRD Sportivo'
enginesize=1364
enginebhp=67
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

Case Mahindra TUV 300 P4 1279cc, 120bhp

carname='Mahindra TUV 300 P4'
enginesize=1279
enginebhp=120
engineName_df.loc[engineName_df['Name'] == carname,'Engine'] = engsize
engineName_df.loc[engineName_df['Name'] == carname,'Power'] = enginebhp

#Visualize the dataframe after changes
engineName_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	1500.00	NaN	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	1300.00	NaN	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	1300.00	NaN	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	3000.00	NaN	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	1300.00	NaN	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	1300.00	NaN	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	1500.00	NaN	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14
1460	Land Rover Range Rover Sport 2005 2012 Sport	Coimbatore	69078	Manual	First	4400.00	296	NaN	120.00	40.88	Land Rover	12	0	0	0	0	1	14
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	1300.00	NaN	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133
2096	Hyundai Santro LP zipPlus	Coimbatore	52146	Manual	First	4400.00	62	NaN	4.55	1.93	Hyundai	16	0	0	0	0	1	14
2264	Toyota Etios Liva V	Pune	24500	Manual	Second	4400.00	79	NaN	8.53	2.95	Toyota	8	0	0	0	0	1	249
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	1300.00	NaN	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	1300.00	NaN	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133
2530	BMW 5 Series 520d Sedan	Kochi	64158	Automatic	First	1995.00	188	NaN	67.87	17.89	BMW	6	0	1	0	0	0	163
2542	Hyundai Santro GLS II - Euro II	Bangalore	65000	Manual	Second	4400.00	62	NaN	4.55	3.15	Hyundai	9	0	0	0	0	1	14
2623	BMW 5 Series 520d Sedan	Pune	95000	Automatic	Second	1995.00	188	NaN	67.87	18.00	BMW	8	0	1	0	0	0	163
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	1300.00	NaN	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133
2737	Maruti Wagon R Vx	Jaipur	200000	Manual	First	4400.00	65.71	NaN	5.51	0.70	Maruti	19	0	0	0	0	1	188
2780	Hyundai Santro GLS II - Euro II	Pune	100000	Manual	First	4400.00	62	NaN	4.55	1.60	Hyundai	11	0	0	0	0	1	14
2842	Hyundai Santro GLS II - Euro II	Bangalore	43000	Manual	First	4400.00	62	NaN	4.55	3.25	Hyundai	8	0	0	0	0	1	14
3272	BMW 5 Series 520d Sedan	Mumbai	81000	Automatic	Second	1995.00	188	NaN	67.87	10.50	BMW	12	0	1	0	0	0	163
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	1300.00	NaN	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133
3520	BMW 5 Series 520d Sedan	Delhi	90000	Automatic	First	1995.00	188	NaN	67.87	14.50	BMW	8	0	1	0	0	0	163
3522	Hyundai Santro GLS II - Euro II	Kochi	66400	Manual	First	4400.00	62	NaN	4.55	2.66	Hyundai	8	0	0	0	0	1	14
3810	Honda CR-V AT With Sun Roof	Kolkata	27000	Automatic	First	4400.00	185	NaN	33.51	11.99	Honda	7	0	0	0	0	1	33
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	1300.00	NaN	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	3000.00	NaN	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14
4229	Hyundai Santro Xing XG	Bangalore	79000	Manual	Second	4400.00	63	NaN	4.55	1.65	Hyundai	15	0	0	0	0	1	72
4577	BMW 5 Series 520d Sedan	Delhi	72000	Automatic	Third	1995.00	188	NaN	67.87	13.85	BMW	8	0	1	0	0	0	163
4604	Honda Jazz Select Edition	Pune	98000	Manual	First	4400.00	89	NaN	9.60	3.15	Honda	9	0	0	0	0	1	378
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	1200.00	NaN	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328
4712	Hyundai Santro Xing XG	Pune	80000	Manual	Second	4400.00	63	NaN	4.55	0.90	Hyundai	17	0	0	0	0	1	72
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	1400.00	NaN	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	1300.00	NaN	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	1300.00	NaN	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	1500.00	NaN	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	1800.00	NaN	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26
6541	Toyota Etios Liva Diesel TRD Sportivo	Bangalore	56600	Manual	First	4400.00	67	NaN	8.53	NaN	Toyota	8	0	1	0	0	0	132
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	1400.00	NaN	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89
6633	Mahindra TUV 300 P4	Kolkata	27000	Manual	First	4400.00	120	NaN	18.86	NaN	Mahindra	4	0	1	0	0	0	14
6643	BMW 5 Series 520d Sedan	Bangalore	150000	Automatic	Second	1995.00	188	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	1300.00	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	1400.00	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	1300.00	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133
6880	BMW 5 Series 520d Sedan	Chennai	95000	Automatic	Second	1995.00	188	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163
6902	Toyota Etios Liva V	Kochi	59311	Manual	First	4400.00	79	NaN	8.53	NaN	Toyota	8	0	0	0	0	1	249

#lets drop the nan entries
usedcarworks_df = usedcarworks_df.dropna(subset=['Engine'])
usedcarworks_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
0	Maruti Wagon R LXI CNG	Mumbai	72000	Manual	First	998.00	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Manual	First	1582.00	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1
2	Honda Jazz V	Chennai	46000	Manual	First	1199.00	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2
3	Maruti Ertiga VDI	Chennai	87000	Manual	First	1248.00	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Automatic	Second	1968.00	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7248	Volkswagen Vento Diesel Trendline	Hyderabad	89411	Manual	First	1598.00	103.6 bhp	5.00	13.23	NaN	Volkswagen	9	0	1	0	0	0	8
7249	Volkswagen Polo GT TSI	Mumbai	59000	Automatic	First	1197.00	103.6 bhp	5.00	10.15	NaN	Volkswagen	5	0	0	0	0	1	224
7250	Nissan Micra Diesel XV	Kolkata	28000	Manual	First	1461.00	63.1 bhp	5.00	9.47	NaN	Nissan	8	0	1	0	0	0	6
7251	Volkswagen Polo GT TSI	Pune	52262	Automatic	Third	1197.00	103.6 bhp	5.00	10.15	NaN	Volkswagen	7	0	0	0	0	1	107
7252	Mercedes-Benz E-Class 2009-2013 E 220 CDI Avan...	Kochi	72443	Automatic	First	2148.00	170 bhp	5.00	86.97	NaN	Mercedes-Benz	6	0	1	0	0	0	252

7206 rows × 18 columns

#add dataframes

usedcarworks_df = usedcarworks_df.append(engineName_df)
usedcarworks_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
0	Maruti Wagon R LXI CNG	Mumbai	72000	Manual	First	998.00	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Manual	First	1582.00	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1
2	Honda Jazz V	Chennai	46000	Manual	First	1199.00	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2
3	Maruti Ertiga VDI	Chennai	87000	Manual	First	1248.00	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Automatic	Second	1968.00	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	1300.00	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	1400.00	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	1300.00	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133
6880	BMW 5 Series 520d Sedan	Chennai	95000	Automatic	Second	1995.00	188	NaN	67.87	NaN	BMW	11	0	1	0	0	0	163
6902	Toyota Etios Liva V	Kochi	59311	Manual	First	4400.00	79	NaN	8.53	NaN	Toyota	8	0	0	0	0	1	249

7252 rows × 18 columns

usedcarworks_df["Engine"].isna().value_counts()

False    7252
Name: Engine, dtype: int64

No more nan values for Engine

# sorting by amount of missing values
usedcarworks_df.isnull().sum().sort_values(ascending=False)

Price                 1234
Power                  153
Seats                   53
Brand                    0
Fuel_Type_Petrol         0
Fuel_Type_LPG            0
Fuel_Type_Electric       0
Fuel_Type_Diesel         0
Fuel_Type_CNG            0
Age                      0
Name                     0
Location                 0
New_Price                0
Engine                   0
Owner_Type               0
Transmission             0
Kilometers_Driven        0
Mileage                  0
dtype: int64

A review shows that Price, Power, and Seats have missing values.

Power

#quick view of Power
usedcarworks_df['Power']

0       58.16 bhp
1       126.2 bhp
2        88.7 bhp
3       88.76 bhp
4       140.8 bhp
          ...    
6651          NaN
6677          NaN
6685          NaN
6880          188
6902           79
Name: Power, Length: 7252, dtype: object

#how many 'Power' rows are NaN
usedcarworks_df['Power'].isna().value_counts()

False    7099
True      153
Name: Power, dtype: int64

#find all Power that are nan
usedcarworks_df.loc[usedcarworks_df['Power'].isna()]

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
76	Ford Fiesta 1.4 SXi TDCi	Jaipur	111111	Manual	First	1399.00	NaN	5.00	11.69	2.00	Ford	12	0	1	0	0	0	49
79	Hyundai Santro Xing XL	Hyderabad	87591	Manual	First	1086.00	NaN	5.00	4.55	1.30	Hyundai	15	0	0	0	0	1	14
89	Hyundai Santro Xing XO	Hyderabad	73745	Manual	First	1086.00	NaN	5.00	4.55	2.10	Hyundai	13	0	0	0	0	1	72
120	Hyundai Santro Xing XL eRLX Euro III	Mumbai	102000	Manual	Second	1086.00	NaN	5.00	4.55	0.85	Hyundai	15	0	0	0	0	1	72
143	Hyundai Santro Xing XO eRLX Euro II	Kochi	80759	Manual	Third	1086.00	NaN	5.00	4.55	1.67	Hyundai	12	0	0	0	0	1	72
227	Maruti Swift VDI BSIV	Delhi	47000	Manual	First	1248.00	NaN	5.00	7.88	4.10	Maruti	7	0	1	0	0	0	49
245	Hyundai Santro Xing XO eRLX Euro II	Mumbai	72000	Manual	Third	1086.00	NaN	5.00	4.55	0.95	Hyundai	13	0	0	0	0	1	72
262	Hyundai Santro Xing XL	Hyderabad	99000	Manual	First	1086.00	NaN	5.00	4.55	1.75	Hyundai	14	0	0	0	0	1	14
307	Hyundai Santro Xing XL	Chennai	58000	Manual	Second	1086.00	NaN	5.00	4.55	1.50	Hyundai	14	0	0	0	0	1	14
308	Maruti Swift VDI BSIV	Kolkata	60020	Manual	First	1248.00	NaN	5.00	7.88	2.50	Maruti	9	0	1	0	0	0	49
386	Hyundai Santro Xing XL eRLX Euro III	Hyderabad	50958	Manual	First	1086.00	NaN	5.00	4.55	1.95	Hyundai	13	0	0	0	0	1	72
424	Volkswagen Jetta 2007-2011 1.9 L TDI	Hyderabad	42021	Manual	First	1968.00	NaN	5.00	10.15	5.45	Volkswagen	10	0	1	0	0	0	14
428	Hyundai Santro Xing XO eRLX Euro II	Pune	108000	Manual	Second	1086.00	NaN	5.00	4.55	1.20	Hyundai	14	0	0	0	0	1	72
443	Hyundai Santro GLS I - Euro I	Coimbatore	50243	Manual	First	1086.00	NaN	5.00	4.55	3.35	Hyundai	8	0	0	0	0	1	14
472	Ford Fiesta 1.6 SXI ABS Duratec	Chennai	80524	Manual	Second	1596.00	NaN	5.00	11.69	2.50	Ford	11	0	0	0	0	1	114
575	Hyundai Santro Xing XO	Chennai	83072	Manual	First	1086.00	NaN	5.00	4.55	1.40	Hyundai	14	0	0	0	0	1	72
631	Hyundai Santro LS zipPlus	Chennai	70000	Manual	Third	1086.00	NaN	5.00	4.55	0.65	Hyundai	18	0	0	0	0	1	14
647	Hyundai Santro Xing XP	Jaipur	200000	Manual	First	1086.00	NaN	5.00	4.55	0.80	Hyundai	16	0	0	0	0	1	14
648	Tata Indica DLS	Jaipur	82000	Manual	Second	1405.00	NaN	5.00	7.63	0.75	Tata	11	0	1	0	0	0	15
739	Maruti Swift VDI BSIV	Bangalore	60000	Manual	Second	1248.00	NaN	5.00	7.88	3.40	Maruti	13	0	1	0	0	0	49
748	Hyundai Santro Xing XO	Hyderabad	71297	Manual	First	1086.00	NaN	5.00	4.55	2.20	Hyundai	13	0	0	0	0	1	72
829	Toyota Etios Liva G	Mumbai	29605	Manual	First	1197.00	NaN	5.00	8.53	2.44	Toyota	8	0	0	0	0	1	249
915	Smart Fortwo CDI AT	Pune	103000	Automatic	Second	799.00	NaN	2.00	11.30	3.00	Smart	12	0	1	0	0	0	14
926	Porsche Cayman 2009-2012 S	Hyderabad	10000	Manual	First	3436.00	NaN	2.00	136.00	40.00	Porsche	10	0	0	0	0	1	266
934	Toyota Etios GD	Kolkata	60000	Manual	First	1364.00	NaN	5.00	8.53	2.95	Toyota	8	0	1	0	0	0	132
1068	Toyota Etios Liva GD	Pune	225000	Manual	Second	1364.00	NaN	5.00	8.53	2.60	Toyota	8	0	1	0	0	0	132
1143	Maruti Swift VDI BSIV	Bangalore	23000	Manual	First	1248.00	NaN	5.00	7.88	5.60	Maruti	7	0	1	0	0	0	49
1153	Hyundai Santro Xing XL eRLX Euro III	Kolkata	43206	Manual	First	1086.00	NaN	5.00	4.55	0.99	Hyundai	13	0	0	0	0	1	72
1271	Hyundai Santro GLS I - Euro II	Jaipur	89000	Manual	Second	999.00	NaN	5.00	4.55	1.60	Hyundai	11	0	0	0	0	1	14
1319	Fiat Petra 1.2 EL	Pune	120000	Manual	Second	1242.00	NaN	5.00	9.07	0.85	Fiat	15	0	0	0	0	1	205
1345	Maruti Baleno Vxi	Pune	70000	Manual	First	1590.00	NaN	5.00	8.80	1.30	Maruti	15	0	0	0	0	1	14
1388	Maruti Swift VDI BSIV	Pune	37000	Manual	First	1248.00	NaN	5.00	7.88	4.85	Maruti	7	0	1	0	0	0	49
1419	Hyundai Santro Xing XL	Chennai	82000	Manual	Second	1086.00	NaN	5.00	4.55	1.35	Hyundai	13	0	0	0	0	1	14
1555	Chevrolet Optra 1.6 Elite	Pune	150000	Manual	First	1598.00	NaN	5.00	11.30	1.50	Chevrolet	15	0	0	0	0	1	129
1578	Mahindra Bolero ZLX	Chennai	35000	Manual	First	2523.00	NaN	7.00	11.01	7.00	Mahindra	8	0	1	0	0	0	221
1649	Toyota Etios Liva GD	Pune	32000	Manual	First	1364.00	NaN	5.00	8.53	3.85	Toyota	9	0	1	0	0	0	132
1672	Nissan Micra Diesel	Jaipur	80000	Manual	Second	1461.00	NaN	5.00	9.47	2.10	Nissan	9	0	1	0	0	0	284
1857	Hyundai Santro DX	Hyderabad	96000	Manual	Second	999.00	NaN	5.00	4.55	2.20	Hyundai	13	0	0	0	0	1	14
1999	Mercedes-Benz E-Class 220 CDI	Pune	121000	Manual	First	2148.00	NaN	5.00	86.97	5.00	Mercedes-Benz	19	0	1	0	0	0	89
2053	Mahindra Jeep MM 550 PE	Hyderabad	26000	Manual	First	2498.00	NaN	6.00	18.86	6.99	Mahindra	11	0	1	0	0	0	14
2130	Hyundai Santro GLS I - Euro II	Coimbatore	51019	Manual	First	999.00	NaN	5.00	4.55	3.48	Hyundai	8	0	0	0	0	1	14
2164	Maruti Swift VDI BSIV W ABS	Hyderabad	86514	Manual	First	1248.00	NaN	5.00	7.88	4.25	Maruti	10	0	1	0	0	0	49
2262	Toyota Etios GD	Bangalore	58253	Manual	Second	1364.00	NaN	5.00	8.53	4.45	Toyota	8	0	1	0	0	0	132
2267	Toyota Qualis RS E2	Pune	215750	Manual	Second	2446.00	NaN	10.00	24.01	3.50	Toyota	16	0	1	0	0	0	14
2305	Porsche Cayman 2009-2012 S tiptronic	Mumbai	8000	Automatic	First	3436.00	NaN	2.00	136.00	43.00	Porsche	9	0	0	0	0	1	266
2343	Hyundai Santro AT	Hyderabad	74483	Automatic	First	999.00	NaN	5.00	4.55	2.30	Hyundai	14	0	0	0	0	1	14
2369	Maruti Estilo LXI	Chennai	56000	Manual	Second	1061.00	NaN	NaN	7.88	1.50	Maruti	12	0	0	0	0	1	284
2393	Toyota Etios GD	Chennai	150000	Manual	Third	1364.00	NaN	5.00	8.53	3.00	Toyota	8	0	1	0	0	0	132
2441	Toyota Etios Liva G	Kolkata	42118	Manual	First	1197.00	NaN	5.00	8.53	2.19	Toyota	9	0	0	0	0	1	249
2450	Tata Indica DLS	Kolkata	75648	Manual	First	1405.00	NaN	5.00	7.63	0.82	Tata	11	0	1	0	0	0	15
2497	Maruti Swift VDI BSIV	Hyderabad	68800	Manual	First	1248.00	NaN	5.00	7.88	5.00	Maruti	7	0	1	0	0	0	49
2501	Maruti Swift VDI BSIV	Hyderabad	51787	Manual	First	1248.00	NaN	5.00	7.88	6.45	Maruti	4	0	1	0	0	0	49
2527	Hyundai Santro Xing XO CNG	Pune	100000	Manual	Third	1086.00	NaN	5.00	4.55	1.20	Hyundai	15	1	0	0	0	0	72
2579	Hyundai Santro Xing XG eRLX Euro III	Mumbai	50888	Manual	First	1086.00	NaN	5.00	4.55	1.15	Hyundai	14	0	0	0	0	1	72
2597	Hyundai Santro Xing XP	Pune	70000	Manual	First	1086.00	NaN	5.00	4.55	1.12	Hyundai	13	0	0	0	0	1	14
2635	Hyundai Santro Xing XG eRLX Euro III	Kochi	58815	Manual	First	1086.00	NaN	5.00	4.55	1.99	Hyundai	13	0	0	0	0	1	72
2640	Maruti Swift VDI BSIV	Pune	121000	Manual	Second	1248.00	NaN	5.00	7.88	2.35	Maruti	13	0	1	0	0	0	49
2891	Maruti Swift VDI BSIV	Bangalore	58291	Manual	First	1248.00	NaN	5.00	7.88	5.00	Maruti	7	0	1	0	0	0	49
3033	Hyundai Santro Xing XP	Jaipur	120000	Manual	First	1086.00	NaN	5.00	4.55	1.15	Hyundai	15	0	0	0	0	1	14
3061	Hyundai Santro GS	Ahmedabad	58000	Manual	Second	999.00	NaN	5.00	4.55	1.51	Hyundai	15	0	0	0	0	1	14
3104	Maruti Swift VDI BSIV	Kolkata	41300	Manual	First	1248.00	NaN	5.00	7.88	3.38	Maruti	7	0	1	0	0	0	49
3189	Hyundai Santro GS zipDrive - Euro II	Chennai	67000	Manual	Third	999.00	NaN	5.00	4.55	1.20	Hyundai	18	0	0	0	0	1	14
3247	Toyota Etios GD	Chennai	79000	Manual	First	1364.00	NaN	5.00	8.53	4.67	Toyota	9	0	1	0	0	0	132
3290	Ford Fiesta 1.4 SXi TDCi	Hyderabad	83000	Manual	Second	1399.00	NaN	5.00	11.69	2.40	Ford	12	0	1	0	0	0	49
3439	Maruti Swift VDI BSIV W ABS	Pune	63600	Manual	Second	1248.00	NaN	5.00	7.88	3.10	Maruti	10	0	1	0	0	0	49
3516	Hyundai Santro GLS I - Euro I	Pune	65400	Manual	First	1086.00	NaN	5.00	4.55	2.10	Hyundai	9	0	0	0	0	1	14
3533	Nissan Teana 230jM	Mumbai	86340	Automatic	Second	2349.00	NaN	5.00	11.33	3.25	Nissan	12	0	0	0	0	1	389
3589	Tata Indica DLS	Hyderabad	100000	Manual	Second	1405.00	NaN	5.00	7.63	1.30	Tata	15	0	1	0	0	0	15
3628	Maruti Swift VDI BSIV W ABS	Coimbatore	70478	Manual	First	1248.00	NaN	5.00	7.88	6.86	Maruti	4	0	1	0	0	0	49
3638	Honda CR-V Sport	Hyderabad	67758	Manual	First	2354.00	NaN	5.00	33.51	9.50	Honda	10	0	0	0	0	1	88
3645	Hyundai Santro Xing XP	Bangalore	167000	Manual	First	1086.00	NaN	5.00	4.55	1.35	Hyundai	16	0	0	0	0	1	14
3669	Hyundai Santro Xing XO	Hyderabad	57309	Manual	First	1086.00	NaN	5.00	4.55	2.10	Hyundai	13	0	0	0	0	1	72
3733	Maruti Esteem Vxi	Delhi	69496	Manual	First	1298.00	NaN	5.00	7.88	1.10	Maruti	15	0	0	0	0	1	231
3800	Ford Endeavour Hurricane LE	Mumbai	129000	Automatic	First	2953.00	NaN	NaN	35.29	7.00	Ford	8	0	1	0	0	0	27
3882	Maruti Estilo LXI	Kolkata	40000	Manual	Second	1061.00	NaN	NaN	7.88	2.50	Maruti	10	0	0	0	0	1	284
3898	Toyota Etios GD	Coimbatore	46496	Manual	First	1364.00	NaN	5.00	8.53	5.30	Toyota	7	0	1	0	0	0	132
3930	Maruti Swift VDI BSIV W ABS	Bangalore	62588	Manual	First	1248.00	NaN	5.00	7.88	5.95	Maruti	5	0	1	0	0	0	49
3999	Audi A4 3.2 FSI Tiptronic Quattro	Hyderabad	125000	Automatic	First	3197.00	NaN	0.00	53.14	18.00	Audi	8	0	0	0	0	1	226
4077	Maruti Baleno LXI - BSIII	Kochi	70362	Manual	Second	1590.00	NaN	5.00	8.80	1.81	Maruti	14	0	0	0	0	1	125
4080	Toyota Etios Liva GD	Mumbai	74000	Manual	First	1364.00	NaN	5.00	8.53	3.60	Toyota	9	0	1	0	0	0	132
4351	Maruti Esteem LX BSII	Jaipur	55000	Manual	Third	1298.00	NaN	5.00	7.88	0.70	Maruti	17	0	0	0	0	1	231
4354	Toyota Etios Liva GD	Delhi	72351	Manual	First	1364.00	NaN	5.00	8.53	2.65	Toyota	8	0	1	0	0	0	132
4629	Fiat Siena 1.2 ELX	Jaipur	70000	Manual	Third	1242.00	NaN	5.00	9.07	0.55	Fiat	19	0	0	0	0	1	14
4709	Maruti 1000 AC	Hyderabad	104000	Manual	Second	970.00	NaN	5.00	7.88	0.85	Maruti	22	0	0	0	0	1	89
4714	Mercedes-Benz E-Class 220 CDI	Ahmedabad	78000	Manual	Second	2148.00	NaN	5.00	86.97	14.50	Mercedes-Benz	9	0	1	0	0	0	89
4744	Hyundai Santro Xing XL eRLX Euro III	Kolkata	45000	Manual	First	1086.00	NaN	5.00	4.55	0.95	Hyundai	15	0	0	0	0	1	72
4830	Maruti Esteem Vxi	Chennai	95000	Manual	First	1298.00	NaN	5.00	7.88	0.80	Maruti	16	0	0	0	0	1	231
4886	Hyundai Santro Xing XL AT eRLX Euro II	Hyderabad	76000	Automatic	First	1086.00	NaN	5.00	4.55	2.40	Hyundai	13	0	0	0	0	1	72
4900	Hyundai Santro Xing XL AT eRLX Euro III	Hyderabad	71000	Automatic	First	1086.00	NaN	5.00	4.55	2.40	Hyundai	13	0	0	0	0	1	72
4954	Toyota Etios Liva GD	Coimbatore	62601	Manual	First	1364.00	NaN	5.00	8.53	4.67	Toyota	7	0	1	0	0	0	132
5065	Maruti Swift VDI BSIV W ABS	Hyderabad	64000	Manual	First	1248.00	NaN	5.00	7.88	5.99	Maruti	4	0	1	0	0	0	49
5119	Hyundai Santro Xing XP	Kolkata	45500	Manual	Second	1086.00	NaN	5.00	4.55	1.17	Hyundai	12	0	0	0	0	1	14
5228	Hyundai Santro Xing XL eRLX Euro III	Pune	126000	Manual	Second	1086.00	NaN	5.00	4.55	1.20	Hyundai	15	0	0	0	0	1	72
5426	Hyundai Santro Xing XL	Chennai	85000	Manual	Third	1086.00	NaN	5.00	4.55	1.30	Hyundai	14	0	0	0	0	1	14
5438	Ford Endeavour XLT TDCi 4X4	Mumbai	116000	Manual	Third	2499.00	NaN	7.00	35.29	3.75	Ford	13	0	1	0	0	0	330
5458	Maruti Swift VDI BSIV	Hyderabad	81609	Manual	First	1248.00	NaN	5.00	7.88	5.55	Maruti	6	0	1	0	0	0	49
5529	Hyundai Santro LP - Euro II	Chennai	105000	Manual	First	999.00	NaN	5.00	4.55	1.75	Hyundai	15	0	0	0	0	1	14
5533	Hyundai Santro Xing XO eRLX Euro II	Pune	72000	Manual	Second	1086.00	NaN	5.00	4.55	1.11	Hyundai	13	0	0	0	0	1	72
5647	Toyota Qualis Fleet A3	Mumbai	227000	Manual	Fourth & Above	2446.00	NaN	8.00	24.01	2.20	Toyota	19	0	1	0	0	0	14
5755	Hyundai Santro Xing XL eRLX Euro III	Ahmedabad	51000	Manual	First	1086.00	NaN	5.00	4.55	1.75	Hyundai	12	0	0	0	0	1	72
5759	Hyundai Santro Xing XL eRLX Euro III	Kolkata	46707	Manual	First	1086.00	NaN	5.00	4.55	0.99	Hyundai	13	0	0	0	0	1	72
5861	Hyundai Santro Xing XO	Chennai	79000	Manual	First	1086.00	NaN	5.00	4.55	1.85	Hyundai	13	0	0	0	0	1	72
5873	Hyundai Santro Xing XO eRLX Euro II	Pune	47200	Manual	Second	1086.00	NaN	5.00	4.55	1.20	Hyundai	14	0	0	0	0	1	72
5893	Maruti Estilo LXI	Chennai	51000	Manual	Second	1061.00	NaN	NaN	7.88	1.75	Maruti	12	0	0	0	0	1	284
5925	Skoda Laura Classic 1.8 TSI	Pune	85000	Manual	First	1798.00	NaN	5.00	25.83	2.85	Skoda	10	0	0	0	0	1	26
5943	Mahindra Jeep MM 540 DP	Chennai	75000	Manual	First	2112.00	NaN	6.00	18.86	1.70	Mahindra	18	0	1	0	0	0	14
5985	Toyota Etios GD	Delhi	70000	Manual	First	1364.00	NaN	5.00	8.53	3.88	Toyota	7	0	1	0	0	0	132
6022	Toyota Etios Liva GD	Hyderabad	139000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6093	Hyundai Santro Xing XL	Bangalore	47000	Manual	Second	1086.00	NaN	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	14
6141	Toyota Etios Liva GD	Jaipur	121134	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6162	Ford Fiesta 1.4 SXI Duratorq	Jaipur	135000	Manual	First	1399.00	NaN	5.00	11.69	NaN	Ford	12	0	1	0	0	0	49
6210	Tata Indica DLS	Chennai	140000	Manual	Second	1405.00	NaN	5.00	7.63	NaN	Tata	14	0	1	0	0	0	15
6216	Hindustan Motors Contessa 2.0 DSL	Pune	65000	Manual	Second	1995.00	NaN	5.00	11.30	NaN	Hindustan	24	0	1	0	0	0	233
6278	Toyota Etios GD	Bangalore	90000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6386	Nissan Teana 230jM	Coimbatore	67038	Automatic	First	2349.00	NaN	5.00	11.33	NaN	Nissan	11	0	0	0	0	1	389
6428	Nissan Teana 230jM	Chennai	63288	Automatic	First	2349.00	NaN	5.00	11.33	NaN	Nissan	12	0	0	0	0	1	389
6439	Hyundai Santro GLS I - Euro II	Bangalore	43189	Manual	First	999.00	NaN	5.00	4.55	NaN	Hyundai	9	0	0	0	0	1	14
6445	Maruti Swift VDI BSIV W ABS	Coimbatore	69564	Manual	First	1248.00	NaN	5.00	7.88	NaN	Maruti	4	0	1	0	0	0	49
6454	Hyundai Santro LS zipDrive Euro I	Chennai	120000	Manual	Fourth & Above	999.00	NaN	5.00	4.55	NaN	Hyundai	18	0	0	0	0	1	14
6576	Hyundai Santro LS zipPlus	Kolkata	80000	Manual	First	1086.00	NaN	5.00	4.55	NaN	Hyundai	18	0	0	0	0	1	14
6591	Ford Fiesta 1.4 SXi TDCi	Pune	134000	Manual	First	1399.00	NaN	5.00	11.69	NaN	Ford	11	0	1	0	0	0	49
6625	Maruti Swift VDI BSIV	Kolkata	72000	Manual	First	1248.00	NaN	5.00	7.88	NaN	Maruti	8	0	1	0	0	0	49
6697	Hyundai Santro Xing XL	Jaipur	85000	Manual	Second	1086.00	NaN	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	14
6723	Ford Fiesta 1.4 SXi TDCi	Kolkata	39408	Manual	First	1399.00	NaN	5.00	11.69	NaN	Ford	11	0	1	0	0	0	49
6891	Toyota Etios GD	Kolkata	60000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	7	0	1	0	0	0	132
6896	Toyota Etios Liva GD	Hyderabad	86000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	7	0	1	0	0	0	132
6947	Toyota Etios Liva G	Delhi	77800	Manual	First	1197.00	NaN	5.00	8.53	NaN	Toyota	8	0	0	0	0	1	249
7092	Hyundai Santro Xing XG AT eRLX Euro III	Coimbatore	64168	Automatic	First	1086.00	NaN	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	72
7145	Toyota Etios Liva G	Kolkata	37212	Manual	First	1197.00	NaN	5.00	8.53	NaN	Toyota	8	0	0	0	0	1	249
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	1500.00	NaN	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	1300.00	NaN	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	1300.00	NaN	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	3000.00	NaN	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	1300.00	NaN	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	1300.00	NaN	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	1500.00	NaN	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	1300.00	NaN	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	1300.00	NaN	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	1300.00	NaN	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	1300.00	NaN	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	1300.00	NaN	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	1300.00	NaN	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	3000.00	NaN	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	1200.00	NaN	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	1400.00	NaN	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	1300.00	NaN	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	1300.00	NaN	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	1500.00	NaN	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	1800.00	NaN	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	1400.00	NaN	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	1300.00	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	1400.00	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	1300.00	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133

There are lots of nans in Power

#create a dataframe for power nan
powernull_df= pd.DataFrame(usedcarworks_df.loc[usedcarworks_df['Power'].isna()])
powernull_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
76	Ford Fiesta 1.4 SXi TDCi	Jaipur	111111	Manual	First	1399.00	NaN	5.00	11.69	2.00	Ford	12	0	1	0	0	0	49
79	Hyundai Santro Xing XL	Hyderabad	87591	Manual	First	1086.00	NaN	5.00	4.55	1.30	Hyundai	15	0	0	0	0	1	14
89	Hyundai Santro Xing XO	Hyderabad	73745	Manual	First	1086.00	NaN	5.00	4.55	2.10	Hyundai	13	0	0	0	0	1	72
120	Hyundai Santro Xing XL eRLX Euro III	Mumbai	102000	Manual	Second	1086.00	NaN	5.00	4.55	0.85	Hyundai	15	0	0	0	0	1	72
143	Hyundai Santro Xing XO eRLX Euro II	Kochi	80759	Manual	Third	1086.00	NaN	5.00	4.55	1.67	Hyundai	12	0	0	0	0	1	72
227	Maruti Swift VDI BSIV	Delhi	47000	Manual	First	1248.00	NaN	5.00	7.88	4.10	Maruti	7	0	1	0	0	0	49
245	Hyundai Santro Xing XO eRLX Euro II	Mumbai	72000	Manual	Third	1086.00	NaN	5.00	4.55	0.95	Hyundai	13	0	0	0	0	1	72
262	Hyundai Santro Xing XL	Hyderabad	99000	Manual	First	1086.00	NaN	5.00	4.55	1.75	Hyundai	14	0	0	0	0	1	14
307	Hyundai Santro Xing XL	Chennai	58000	Manual	Second	1086.00	NaN	5.00	4.55	1.50	Hyundai	14	0	0	0	0	1	14
308	Maruti Swift VDI BSIV	Kolkata	60020	Manual	First	1248.00	NaN	5.00	7.88	2.50	Maruti	9	0	1	0	0	0	49
386	Hyundai Santro Xing XL eRLX Euro III	Hyderabad	50958	Manual	First	1086.00	NaN	5.00	4.55	1.95	Hyundai	13	0	0	0	0	1	72
424	Volkswagen Jetta 2007-2011 1.9 L TDI	Hyderabad	42021	Manual	First	1968.00	NaN	5.00	10.15	5.45	Volkswagen	10	0	1	0	0	0	14
428	Hyundai Santro Xing XO eRLX Euro II	Pune	108000	Manual	Second	1086.00	NaN	5.00	4.55	1.20	Hyundai	14	0	0	0	0	1	72
443	Hyundai Santro GLS I - Euro I	Coimbatore	50243	Manual	First	1086.00	NaN	5.00	4.55	3.35	Hyundai	8	0	0	0	0	1	14
472	Ford Fiesta 1.6 SXI ABS Duratec	Chennai	80524	Manual	Second	1596.00	NaN	5.00	11.69	2.50	Ford	11	0	0	0	0	1	114
575	Hyundai Santro Xing XO	Chennai	83072	Manual	First	1086.00	NaN	5.00	4.55	1.40	Hyundai	14	0	0	0	0	1	72
631	Hyundai Santro LS zipPlus	Chennai	70000	Manual	Third	1086.00	NaN	5.00	4.55	0.65	Hyundai	18	0	0	0	0	1	14
647	Hyundai Santro Xing XP	Jaipur	200000	Manual	First	1086.00	NaN	5.00	4.55	0.80	Hyundai	16	0	0	0	0	1	14
648	Tata Indica DLS	Jaipur	82000	Manual	Second	1405.00	NaN	5.00	7.63	0.75	Tata	11	0	1	0	0	0	15
739	Maruti Swift VDI BSIV	Bangalore	60000	Manual	Second	1248.00	NaN	5.00	7.88	3.40	Maruti	13	0	1	0	0	0	49
748	Hyundai Santro Xing XO	Hyderabad	71297	Manual	First	1086.00	NaN	5.00	4.55	2.20	Hyundai	13	0	0	0	0	1	72
829	Toyota Etios Liva G	Mumbai	29605	Manual	First	1197.00	NaN	5.00	8.53	2.44	Toyota	8	0	0	0	0	1	249
915	Smart Fortwo CDI AT	Pune	103000	Automatic	Second	799.00	NaN	2.00	11.30	3.00	Smart	12	0	1	0	0	0	14
926	Porsche Cayman 2009-2012 S	Hyderabad	10000	Manual	First	3436.00	NaN	2.00	136.00	40.00	Porsche	10	0	0	0	0	1	266
934	Toyota Etios GD	Kolkata	60000	Manual	First	1364.00	NaN	5.00	8.53	2.95	Toyota	8	0	1	0	0	0	132
1068	Toyota Etios Liva GD	Pune	225000	Manual	Second	1364.00	NaN	5.00	8.53	2.60	Toyota	8	0	1	0	0	0	132
1143	Maruti Swift VDI BSIV	Bangalore	23000	Manual	First	1248.00	NaN	5.00	7.88	5.60	Maruti	7	0	1	0	0	0	49
1153	Hyundai Santro Xing XL eRLX Euro III	Kolkata	43206	Manual	First	1086.00	NaN	5.00	4.55	0.99	Hyundai	13	0	0	0	0	1	72
1271	Hyundai Santro GLS I - Euro II	Jaipur	89000	Manual	Second	999.00	NaN	5.00	4.55	1.60	Hyundai	11	0	0	0	0	1	14
1319	Fiat Petra 1.2 EL	Pune	120000	Manual	Second	1242.00	NaN	5.00	9.07	0.85	Fiat	15	0	0	0	0	1	205
1345	Maruti Baleno Vxi	Pune	70000	Manual	First	1590.00	NaN	5.00	8.80	1.30	Maruti	15	0	0	0	0	1	14
1388	Maruti Swift VDI BSIV	Pune	37000	Manual	First	1248.00	NaN	5.00	7.88	4.85	Maruti	7	0	1	0	0	0	49
1419	Hyundai Santro Xing XL	Chennai	82000	Manual	Second	1086.00	NaN	5.00	4.55	1.35	Hyundai	13	0	0	0	0	1	14
1555	Chevrolet Optra 1.6 Elite	Pune	150000	Manual	First	1598.00	NaN	5.00	11.30	1.50	Chevrolet	15	0	0	0	0	1	129
1578	Mahindra Bolero ZLX	Chennai	35000	Manual	First	2523.00	NaN	7.00	11.01	7.00	Mahindra	8	0	1	0	0	0	221
1649	Toyota Etios Liva GD	Pune	32000	Manual	First	1364.00	NaN	5.00	8.53	3.85	Toyota	9	0	1	0	0	0	132
1672	Nissan Micra Diesel	Jaipur	80000	Manual	Second	1461.00	NaN	5.00	9.47	2.10	Nissan	9	0	1	0	0	0	284
1857	Hyundai Santro DX	Hyderabad	96000	Manual	Second	999.00	NaN	5.00	4.55	2.20	Hyundai	13	0	0	0	0	1	14
1999	Mercedes-Benz E-Class 220 CDI	Pune	121000	Manual	First	2148.00	NaN	5.00	86.97	5.00	Mercedes-Benz	19	0	1	0	0	0	89
2053	Mahindra Jeep MM 550 PE	Hyderabad	26000	Manual	First	2498.00	NaN	6.00	18.86	6.99	Mahindra	11	0	1	0	0	0	14
2130	Hyundai Santro GLS I - Euro II	Coimbatore	51019	Manual	First	999.00	NaN	5.00	4.55	3.48	Hyundai	8	0	0	0	0	1	14
2164	Maruti Swift VDI BSIV W ABS	Hyderabad	86514	Manual	First	1248.00	NaN	5.00	7.88	4.25	Maruti	10	0	1	0	0	0	49
2262	Toyota Etios GD	Bangalore	58253	Manual	Second	1364.00	NaN	5.00	8.53	4.45	Toyota	8	0	1	0	0	0	132
2267	Toyota Qualis RS E2	Pune	215750	Manual	Second	2446.00	NaN	10.00	24.01	3.50	Toyota	16	0	1	0	0	0	14
2305	Porsche Cayman 2009-2012 S tiptronic	Mumbai	8000	Automatic	First	3436.00	NaN	2.00	136.00	43.00	Porsche	9	0	0	0	0	1	266
2343	Hyundai Santro AT	Hyderabad	74483	Automatic	First	999.00	NaN	5.00	4.55	2.30	Hyundai	14	0	0	0	0	1	14
2369	Maruti Estilo LXI	Chennai	56000	Manual	Second	1061.00	NaN	NaN	7.88	1.50	Maruti	12	0	0	0	0	1	284
2393	Toyota Etios GD	Chennai	150000	Manual	Third	1364.00	NaN	5.00	8.53	3.00	Toyota	8	0	1	0	0	0	132
2441	Toyota Etios Liva G	Kolkata	42118	Manual	First	1197.00	NaN	5.00	8.53	2.19	Toyota	9	0	0	0	0	1	249
2450	Tata Indica DLS	Kolkata	75648	Manual	First	1405.00	NaN	5.00	7.63	0.82	Tata	11	0	1	0	0	0	15
2497	Maruti Swift VDI BSIV	Hyderabad	68800	Manual	First	1248.00	NaN	5.00	7.88	5.00	Maruti	7	0	1	0	0	0	49
2501	Maruti Swift VDI BSIV	Hyderabad	51787	Manual	First	1248.00	NaN	5.00	7.88	6.45	Maruti	4	0	1	0	0	0	49
2527	Hyundai Santro Xing XO CNG	Pune	100000	Manual	Third	1086.00	NaN	5.00	4.55	1.20	Hyundai	15	1	0	0	0	0	72
2579	Hyundai Santro Xing XG eRLX Euro III	Mumbai	50888	Manual	First	1086.00	NaN	5.00	4.55	1.15	Hyundai	14	0	0	0	0	1	72
2597	Hyundai Santro Xing XP	Pune	70000	Manual	First	1086.00	NaN	5.00	4.55	1.12	Hyundai	13	0	0	0	0	1	14
2635	Hyundai Santro Xing XG eRLX Euro III	Kochi	58815	Manual	First	1086.00	NaN	5.00	4.55	1.99	Hyundai	13	0	0	0	0	1	72
2640	Maruti Swift VDI BSIV	Pune	121000	Manual	Second	1248.00	NaN	5.00	7.88	2.35	Maruti	13	0	1	0	0	0	49
2891	Maruti Swift VDI BSIV	Bangalore	58291	Manual	First	1248.00	NaN	5.00	7.88	5.00	Maruti	7	0	1	0	0	0	49
3033	Hyundai Santro Xing XP	Jaipur	120000	Manual	First	1086.00	NaN	5.00	4.55	1.15	Hyundai	15	0	0	0	0	1	14
3061	Hyundai Santro GS	Ahmedabad	58000	Manual	Second	999.00	NaN	5.00	4.55	1.51	Hyundai	15	0	0	0	0	1	14
3104	Maruti Swift VDI BSIV	Kolkata	41300	Manual	First	1248.00	NaN	5.00	7.88	3.38	Maruti	7	0	1	0	0	0	49
3189	Hyundai Santro GS zipDrive - Euro II	Chennai	67000	Manual	Third	999.00	NaN	5.00	4.55	1.20	Hyundai	18	0	0	0	0	1	14
3247	Toyota Etios GD	Chennai	79000	Manual	First	1364.00	NaN	5.00	8.53	4.67	Toyota	9	0	1	0	0	0	132
3290	Ford Fiesta 1.4 SXi TDCi	Hyderabad	83000	Manual	Second	1399.00	NaN	5.00	11.69	2.40	Ford	12	0	1	0	0	0	49
3439	Maruti Swift VDI BSIV W ABS	Pune	63600	Manual	Second	1248.00	NaN	5.00	7.88	3.10	Maruti	10	0	1	0	0	0	49
3516	Hyundai Santro GLS I - Euro I	Pune	65400	Manual	First	1086.00	NaN	5.00	4.55	2.10	Hyundai	9	0	0	0	0	1	14
3533	Nissan Teana 230jM	Mumbai	86340	Automatic	Second	2349.00	NaN	5.00	11.33	3.25	Nissan	12	0	0	0	0	1	389
3589	Tata Indica DLS	Hyderabad	100000	Manual	Second	1405.00	NaN	5.00	7.63	1.30	Tata	15	0	1	0	0	0	15
3628	Maruti Swift VDI BSIV W ABS	Coimbatore	70478	Manual	First	1248.00	NaN	5.00	7.88	6.86	Maruti	4	0	1	0	0	0	49
3638	Honda CR-V Sport	Hyderabad	67758	Manual	First	2354.00	NaN	5.00	33.51	9.50	Honda	10	0	0	0	0	1	88
3645	Hyundai Santro Xing XP	Bangalore	167000	Manual	First	1086.00	NaN	5.00	4.55	1.35	Hyundai	16	0	0	0	0	1	14
3669	Hyundai Santro Xing XO	Hyderabad	57309	Manual	First	1086.00	NaN	5.00	4.55	2.10	Hyundai	13	0	0	0	0	1	72
3733	Maruti Esteem Vxi	Delhi	69496	Manual	First	1298.00	NaN	5.00	7.88	1.10	Maruti	15	0	0	0	0	1	231
3800	Ford Endeavour Hurricane LE	Mumbai	129000	Automatic	First	2953.00	NaN	NaN	35.29	7.00	Ford	8	0	1	0	0	0	27
3882	Maruti Estilo LXI	Kolkata	40000	Manual	Second	1061.00	NaN	NaN	7.88	2.50	Maruti	10	0	0	0	0	1	284
3898	Toyota Etios GD	Coimbatore	46496	Manual	First	1364.00	NaN	5.00	8.53	5.30	Toyota	7	0	1	0	0	0	132
3930	Maruti Swift VDI BSIV W ABS	Bangalore	62588	Manual	First	1248.00	NaN	5.00	7.88	5.95	Maruti	5	0	1	0	0	0	49
3999	Audi A4 3.2 FSI Tiptronic Quattro	Hyderabad	125000	Automatic	First	3197.00	NaN	0.00	53.14	18.00	Audi	8	0	0	0	0	1	226
4077	Maruti Baleno LXI - BSIII	Kochi	70362	Manual	Second	1590.00	NaN	5.00	8.80	1.81	Maruti	14	0	0	0	0	1	125
4080	Toyota Etios Liva GD	Mumbai	74000	Manual	First	1364.00	NaN	5.00	8.53	3.60	Toyota	9	0	1	0	0	0	132
4351	Maruti Esteem LX BSII	Jaipur	55000	Manual	Third	1298.00	NaN	5.00	7.88	0.70	Maruti	17	0	0	0	0	1	231
4354	Toyota Etios Liva GD	Delhi	72351	Manual	First	1364.00	NaN	5.00	8.53	2.65	Toyota	8	0	1	0	0	0	132
4629	Fiat Siena 1.2 ELX	Jaipur	70000	Manual	Third	1242.00	NaN	5.00	9.07	0.55	Fiat	19	0	0	0	0	1	14
4709	Maruti 1000 AC	Hyderabad	104000	Manual	Second	970.00	NaN	5.00	7.88	0.85	Maruti	22	0	0	0	0	1	89
4714	Mercedes-Benz E-Class 220 CDI	Ahmedabad	78000	Manual	Second	2148.00	NaN	5.00	86.97	14.50	Mercedes-Benz	9	0	1	0	0	0	89
4744	Hyundai Santro Xing XL eRLX Euro III	Kolkata	45000	Manual	First	1086.00	NaN	5.00	4.55	0.95	Hyundai	15	0	0	0	0	1	72
4830	Maruti Esteem Vxi	Chennai	95000	Manual	First	1298.00	NaN	5.00	7.88	0.80	Maruti	16	0	0	0	0	1	231
4886	Hyundai Santro Xing XL AT eRLX Euro II	Hyderabad	76000	Automatic	First	1086.00	NaN	5.00	4.55	2.40	Hyundai	13	0	0	0	0	1	72
4900	Hyundai Santro Xing XL AT eRLX Euro III	Hyderabad	71000	Automatic	First	1086.00	NaN	5.00	4.55	2.40	Hyundai	13	0	0	0	0	1	72
4954	Toyota Etios Liva GD	Coimbatore	62601	Manual	First	1364.00	NaN	5.00	8.53	4.67	Toyota	7	0	1	0	0	0	132
5065	Maruti Swift VDI BSIV W ABS	Hyderabad	64000	Manual	First	1248.00	NaN	5.00	7.88	5.99	Maruti	4	0	1	0	0	0	49
5119	Hyundai Santro Xing XP	Kolkata	45500	Manual	Second	1086.00	NaN	5.00	4.55	1.17	Hyundai	12	0	0	0	0	1	14
5228	Hyundai Santro Xing XL eRLX Euro III	Pune	126000	Manual	Second	1086.00	NaN	5.00	4.55	1.20	Hyundai	15	0	0	0	0	1	72
5426	Hyundai Santro Xing XL	Chennai	85000	Manual	Third	1086.00	NaN	5.00	4.55	1.30	Hyundai	14	0	0	0	0	1	14
5438	Ford Endeavour XLT TDCi 4X4	Mumbai	116000	Manual	Third	2499.00	NaN	7.00	35.29	3.75	Ford	13	0	1	0	0	0	330
5458	Maruti Swift VDI BSIV	Hyderabad	81609	Manual	First	1248.00	NaN	5.00	7.88	5.55	Maruti	6	0	1	0	0	0	49
5529	Hyundai Santro LP - Euro II	Chennai	105000	Manual	First	999.00	NaN	5.00	4.55	1.75	Hyundai	15	0	0	0	0	1	14
5533	Hyundai Santro Xing XO eRLX Euro II	Pune	72000	Manual	Second	1086.00	NaN	5.00	4.55	1.11	Hyundai	13	0	0	0	0	1	72
5647	Toyota Qualis Fleet A3	Mumbai	227000	Manual	Fourth & Above	2446.00	NaN	8.00	24.01	2.20	Toyota	19	0	1	0	0	0	14
5755	Hyundai Santro Xing XL eRLX Euro III	Ahmedabad	51000	Manual	First	1086.00	NaN	5.00	4.55	1.75	Hyundai	12	0	0	0	0	1	72
5759	Hyundai Santro Xing XL eRLX Euro III	Kolkata	46707	Manual	First	1086.00	NaN	5.00	4.55	0.99	Hyundai	13	0	0	0	0	1	72
5861	Hyundai Santro Xing XO	Chennai	79000	Manual	First	1086.00	NaN	5.00	4.55	1.85	Hyundai	13	0	0	0	0	1	72
5873	Hyundai Santro Xing XO eRLX Euro II	Pune	47200	Manual	Second	1086.00	NaN	5.00	4.55	1.20	Hyundai	14	0	0	0	0	1	72
5893	Maruti Estilo LXI	Chennai	51000	Manual	Second	1061.00	NaN	NaN	7.88	1.75	Maruti	12	0	0	0	0	1	284
5925	Skoda Laura Classic 1.8 TSI	Pune	85000	Manual	First	1798.00	NaN	5.00	25.83	2.85	Skoda	10	0	0	0	0	1	26
5943	Mahindra Jeep MM 540 DP	Chennai	75000	Manual	First	2112.00	NaN	6.00	18.86	1.70	Mahindra	18	0	1	0	0	0	14
5985	Toyota Etios GD	Delhi	70000	Manual	First	1364.00	NaN	5.00	8.53	3.88	Toyota	7	0	1	0	0	0	132
6022	Toyota Etios Liva GD	Hyderabad	139000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6093	Hyundai Santro Xing XL	Bangalore	47000	Manual	Second	1086.00	NaN	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	14
6141	Toyota Etios Liva GD	Jaipur	121134	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6162	Ford Fiesta 1.4 SXI Duratorq	Jaipur	135000	Manual	First	1399.00	NaN	5.00	11.69	NaN	Ford	12	0	1	0	0	0	49
6210	Tata Indica DLS	Chennai	140000	Manual	Second	1405.00	NaN	5.00	7.63	NaN	Tata	14	0	1	0	0	0	15
6216	Hindustan Motors Contessa 2.0 DSL	Pune	65000	Manual	Second	1995.00	NaN	5.00	11.30	NaN	Hindustan	24	0	1	0	0	0	233
6278	Toyota Etios GD	Bangalore	90000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6386	Nissan Teana 230jM	Coimbatore	67038	Automatic	First	2349.00	NaN	5.00	11.33	NaN	Nissan	11	0	0	0	0	1	389
6428	Nissan Teana 230jM	Chennai	63288	Automatic	First	2349.00	NaN	5.00	11.33	NaN	Nissan	12	0	0	0	0	1	389
6439	Hyundai Santro GLS I - Euro II	Bangalore	43189	Manual	First	999.00	NaN	5.00	4.55	NaN	Hyundai	9	0	0	0	0	1	14
6445	Maruti Swift VDI BSIV W ABS	Coimbatore	69564	Manual	First	1248.00	NaN	5.00	7.88	NaN	Maruti	4	0	1	0	0	0	49
6454	Hyundai Santro LS zipDrive Euro I	Chennai	120000	Manual	Fourth & Above	999.00	NaN	5.00	4.55	NaN	Hyundai	18	0	0	0	0	1	14
6576	Hyundai Santro LS zipPlus	Kolkata	80000	Manual	First	1086.00	NaN	5.00	4.55	NaN	Hyundai	18	0	0	0	0	1	14
6591	Ford Fiesta 1.4 SXi TDCi	Pune	134000	Manual	First	1399.00	NaN	5.00	11.69	NaN	Ford	11	0	1	0	0	0	49
6625	Maruti Swift VDI BSIV	Kolkata	72000	Manual	First	1248.00	NaN	5.00	7.88	NaN	Maruti	8	0	1	0	0	0	49
6697	Hyundai Santro Xing XL	Jaipur	85000	Manual	Second	1086.00	NaN	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	14
6723	Ford Fiesta 1.4 SXi TDCi	Kolkata	39408	Manual	First	1399.00	NaN	5.00	11.69	NaN	Ford	11	0	1	0	0	0	49
6891	Toyota Etios GD	Kolkata	60000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	7	0	1	0	0	0	132
6896	Toyota Etios Liva GD	Hyderabad	86000	Manual	First	1364.00	NaN	5.00	8.53	NaN	Toyota	7	0	1	0	0	0	132
6947	Toyota Etios Liva G	Delhi	77800	Manual	First	1197.00	NaN	5.00	8.53	NaN	Toyota	8	0	0	0	0	1	249
7092	Hyundai Santro Xing XG AT eRLX Euro III	Coimbatore	64168	Automatic	First	1086.00	NaN	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	72
7145	Toyota Etios Liva G	Kolkata	37212	Manual	First	1197.00	NaN	5.00	8.53	NaN	Toyota	8	0	0	0	0	1	249
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	1500.00	NaN	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	1300.00	NaN	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	1300.00	NaN	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	3000.00	NaN	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	1300.00	NaN	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	1300.00	NaN	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	1500.00	NaN	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	1300.00	NaN	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	1300.00	NaN	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	1300.00	NaN	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	1300.00	NaN	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	1300.00	NaN	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	1300.00	NaN	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	3000.00	NaN	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	1200.00	NaN	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	1400.00	NaN	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	1300.00	NaN	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	1300.00	NaN	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	1500.00	NaN	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	1800.00	NaN	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	1400.00	NaN	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	1300.00	NaN	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	1400.00	NaN	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	1300.00	NaN	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133

Some of the cars we already know from before.

Lets take a look at them and solve them one by one.

Visit site: www.carwale.com

powernull_df['Name'].value_counts()

Maruti Swift VDI BSIV                      12
Toyota Etios GD                             8
Maruti Swift 1.3 VXi                        8
Toyota Etios Liva GD                        8
Hyundai Santro Xing XL eRLX Euro III        7
Hyundai Santro Xing XL                      7
Maruti Swift VDI BSIV W ABS                 6
Hyundai Santro Xing XO                      5
Hyundai Santro Xing XO eRLX Euro II         5
Hyundai Santro Xing XP                      5
Toyota Etios Liva G                         4
Ford Fiesta 1.4 SXi TDCi                    4
Tata Indica DLS                             4
Hyundai Santro GLS I - Euro II              3
Nissan Teana 230jM                          3
Honda City 1.5 GXI                          3
Maruti Estilo LXI                           3
Hyundai Santro LS zipPlus                   2
Maruti Swift 1.3 LXI                        2
Maruti Esteem Vxi                           2
Land Rover Range Rover 3.0 D                2
Fiat Punto 1.4 Emotion                      2
Mercedes-Benz E-Class 220 CDI               2
Hyundai Santro Xing XG eRLX Euro III        2
Hyundai Santro GLS I - Euro I               2
Toyota Qualis Fleet A3                      1
Ford Endeavour XLT TDCi 4X4                 1
Hyundai Santro LP - Euro II                 1
Mahindra Jeep MM 540 DP                     1
Hyundai Santro Xing XL AT eRLX Euro III     1
Skoda Laura Classic 1.8 TSI                 1
Honda City 1.3 DX                           1
Ford Fiesta 1.4 SXI Duratorq                1
Hindustan Motors Contessa 2.0 DSL           1
Hyundai Santro LS zipDrive Euro I           1
Hyundai Santro Xing XG AT eRLX Euro III     1
Maruti 1000 AC                              1
Maruti Swift 1.3 ZXI                        1
Maruti Swift 1.3 VXI ABS                    1
Fiat Punto 1.3 Emotion                      1
Fiat Punto 1.2 Dynamic                      1
Skoda Laura 1.8 TSI Ambition                1
Hyundai Santro Xing XL AT eRLX Euro II      1
Hyundai Santro GS                           1
Fiat Siena 1.2 ELX                          1
Hyundai Santro DX                           1
Volkswagen Jetta 2007-2011 1.9 L TDI        1
Ford Fiesta 1.6 SXI ABS Duratec             1
Smart Fortwo CDI AT                         1
Porsche Cayman 2009-2012 S                  1
Fiat Petra 1.2 EL                           1
Maruti Baleno Vxi                           1
Chevrolet Optra 1.6 Elite                   1
Mahindra Bolero ZLX                         1
Nissan Micra Diesel                         1
Mahindra Jeep MM 550 PE                     1
Maruti Esteem LX BSII                       1
Toyota Qualis RS E2                         1
Porsche Cayman 2009-2012 S tiptronic        1
Hyundai Santro AT                           1
Hyundai Santro Xing XO CNG                  1
Hyundai Santro GS zipDrive - Euro II        1
Honda CR-V Sport                            1
Ford Endeavour Hurricane LE                 1
Audi A4 3.2 FSI Tiptronic Quattro           1
Maruti Baleno LXI - BSIII                   1
Hyundai i20 new Sportz AT 1.4               1
Name: Name, dtype: int64

Lets try Case by Case

#case All Maruti Swift VDI BSIV"
carname = "Maruti Swift"
enginesize = 1300
power = 87

#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case All Toyota Etios
carname = "Toyota Etios"
enginesize = 1364
power = 67.05

#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Hyundai Santro Xing XL eRLX Euro III, Hyundai Santro Xing XL , Hyundai Santro Xing XO, etc (All Santros)
carname= "Hyundai Santro"
enginesize = 1086
power = 63

#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Ford Fiesta 1.4 SXi TDCi
carname = "Ford Fiesta 1.4"
enginesize = 1400
power = 68

#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Tata Indica DLS
carname = "Tata Indica DLS"
enginesize = 1405
power = 62

#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Nissan Teana 230jM
carname = "Nissan Teana 230jM"
enginesize = 2349
power = 170
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Toyota Qualis Fleet A3
carname = "Toyota Qualis Fleet A3"
enginesize = 2466
power = 154
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Fiat Punto 1.2 Dynamic
carname = "Fiat Punto 1.2 Dynamic"
power = 67
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Fiat Punto 1.3 Emotion
carname = "Fiat Punto 1.3 Emotion"
power = 91.7
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#caSE Fiat Siena 1.2 ELX
carname = "Fiat Siena 1.2 ELX"
power = 99
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Skoda Laura 1.8
carname = "Skoda Laura 1.8"
power = 157.8
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Land Rover Range Rover 3.0 D
carname = "Land Rover Range Rover 3.0 D"
power = 345.98
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Nissan Micra Diesel
carname = "Nissan Micra Diesel"
power = 65
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Mercedes-Benz E-Class 220 CDI
carname = "Mercedes-Benz E-Class 220 CDI"
power = 191.7
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Mahindra Jeep MM 540 DP
carname = "Mahindra Jeep MM 540 DP"
power = 62
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Porsche Cayman 2009-2012 S tiptronic
carname =  "Porsche Cayman 2009-2012 S tiptronic"
power = 315
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Maruti Baleno LXI - BSIII
carname = "Maruti Baleno"
power = 83.1
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Volkswagen Jetta 2007-2011 1.9 L TDI
carname = "Volkswagen Jetta 2007-2011 1.9 L TDI"
power=104
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Honda CR-V Sport
carname = "Honda CR-V Sport"
power = 145
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Smart Fortwo CDI AT
carname = "Smart Fortwo"
power= 53
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Chevrolet Optra 1.6 Elite
carname = "Chevrolet Optra 1.6"
power= 157.8
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Ford Endeavour Hurricane LE
carname = "Ford Endeavour"
power= 156
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Toyota Qualis RS E2
carname = "Toyota Qualis"
power= 71.02
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Audi A4 3.2 FSI Tiptronic Quattro
carname = "Audi A4 3.2"
power= 252
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Hyundai i20 new Sportz AT 1.4
carname = "Hyundai i20"
power= 88.73
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Honda City 1.5
carname = "Honda City 1.5"
power= 100
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Honda City 1.3
carname = "Honda City 1.3"
power= 84
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Fiat Punto 1.4 Emotion
carname = "Fiat Punto 1.4 Emotion"
power= 88.8
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Porsche Cayman 2009-2012 S
carname = "Porsche Cayman 2009-2012 S"
power= 320
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Mahindra Bolero
carname = "Mahindra Bolero"
power= 70
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Maruti Estilo
carname = "Maruti Estilo"
power= 64
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Tata Indica
carname = "Tata Indica"
power= 64.4
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Maruti Esteem
carname = "Maruti Esteem"
power= 122
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Maruti 1000 AC
carname = "Maruti 1000"
power= 46
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Hindustan Motors Contessa 2.0 DSL
carname = "Hindustan Motors Contessa"
power= 52
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Ford Fiesta 1.6 SXI ABS Duratec
carname = "Ford Fiesta 1.6 SXI ABS Duratec"
power= 99.6
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Fiat Petra 1.2 EL
carname = "Fiat Petra 1.2 EL"
power= 72
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Mahindra Jeep MM 550 PE
carname = "Mahindra Jeep MM 550 PE"
power= 105
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

#case Skoda Laura Classic 1.8 TSI
carname = "Skoda Laura Classic 1.8 TSI"
power= 157.8
#replace by power
powernull_df.loc[powernull_df['Name'].str.contains(carname),'Power'] = power

powernull_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
76	Ford Fiesta 1.4 SXi TDCi	Jaipur	111111	Manual	First	1399.00	68	5.00	11.69	2.00	Ford	12	0	1	0	0	0	49
79	Hyundai Santro Xing XL	Hyderabad	87591	Manual	First	1086.00	63	5.00	4.55	1.30	Hyundai	15	0	0	0	0	1	14
89	Hyundai Santro Xing XO	Hyderabad	73745	Manual	First	1086.00	63	5.00	4.55	2.10	Hyundai	13	0	0	0	0	1	72
120	Hyundai Santro Xing XL eRLX Euro III	Mumbai	102000	Manual	Second	1086.00	63	5.00	4.55	0.85	Hyundai	15	0	0	0	0	1	72
143	Hyundai Santro Xing XO eRLX Euro II	Kochi	80759	Manual	Third	1086.00	63	5.00	4.55	1.67	Hyundai	12	0	0	0	0	1	72
227	Maruti Swift VDI BSIV	Delhi	47000	Manual	First	1248.00	87	5.00	7.88	4.10	Maruti	7	0	1	0	0	0	49
245	Hyundai Santro Xing XO eRLX Euro II	Mumbai	72000	Manual	Third	1086.00	63	5.00	4.55	0.95	Hyundai	13	0	0	0	0	1	72
262	Hyundai Santro Xing XL	Hyderabad	99000	Manual	First	1086.00	63	5.00	4.55	1.75	Hyundai	14	0	0	0	0	1	14
307	Hyundai Santro Xing XL	Chennai	58000	Manual	Second	1086.00	63	5.00	4.55	1.50	Hyundai	14	0	0	0	0	1	14
308	Maruti Swift VDI BSIV	Kolkata	60020	Manual	First	1248.00	87	5.00	7.88	2.50	Maruti	9	0	1	0	0	0	49
386	Hyundai Santro Xing XL eRLX Euro III	Hyderabad	50958	Manual	First	1086.00	63	5.00	4.55	1.95	Hyundai	13	0	0	0	0	1	72
424	Volkswagen Jetta 2007-2011 1.9 L TDI	Hyderabad	42021	Manual	First	1968.00	104	5.00	10.15	5.45	Volkswagen	10	0	1	0	0	0	14
428	Hyundai Santro Xing XO eRLX Euro II	Pune	108000	Manual	Second	1086.00	63	5.00	4.55	1.20	Hyundai	14	0	0	0	0	1	72
443	Hyundai Santro GLS I - Euro I	Coimbatore	50243	Manual	First	1086.00	63	5.00	4.55	3.35	Hyundai	8	0	0	0	0	1	14
472	Ford Fiesta 1.6 SXI ABS Duratec	Chennai	80524	Manual	Second	1596.00	99.60	5.00	11.69	2.50	Ford	11	0	0	0	0	1	114
575	Hyundai Santro Xing XO	Chennai	83072	Manual	First	1086.00	63	5.00	4.55	1.40	Hyundai	14	0	0	0	0	1	72
631	Hyundai Santro LS zipPlus	Chennai	70000	Manual	Third	1086.00	63	5.00	4.55	0.65	Hyundai	18	0	0	0	0	1	14
647	Hyundai Santro Xing XP	Jaipur	200000	Manual	First	1086.00	63	5.00	4.55	0.80	Hyundai	16	0	0	0	0	1	14
648	Tata Indica DLS	Jaipur	82000	Manual	Second	1405.00	64.40	5.00	7.63	0.75	Tata	11	0	1	0	0	0	15
739	Maruti Swift VDI BSIV	Bangalore	60000	Manual	Second	1248.00	87	5.00	7.88	3.40	Maruti	13	0	1	0	0	0	49
748	Hyundai Santro Xing XO	Hyderabad	71297	Manual	First	1086.00	63	5.00	4.55	2.20	Hyundai	13	0	0	0	0	1	72
829	Toyota Etios Liva G	Mumbai	29605	Manual	First	1197.00	67.05	5.00	8.53	2.44	Toyota	8	0	0	0	0	1	249
915	Smart Fortwo CDI AT	Pune	103000	Automatic	Second	799.00	53	2.00	11.30	3.00	Smart	12	0	1	0	0	0	14
926	Porsche Cayman 2009-2012 S	Hyderabad	10000	Manual	First	3436.00	320	2.00	136.00	40.00	Porsche	10	0	0	0	0	1	266
934	Toyota Etios GD	Kolkata	60000	Manual	First	1364.00	67.05	5.00	8.53	2.95	Toyota	8	0	1	0	0	0	132
1068	Toyota Etios Liva GD	Pune	225000	Manual	Second	1364.00	67.05	5.00	8.53	2.60	Toyota	8	0	1	0	0	0	132
1143	Maruti Swift VDI BSIV	Bangalore	23000	Manual	First	1248.00	87	5.00	7.88	5.60	Maruti	7	0	1	0	0	0	49
1153	Hyundai Santro Xing XL eRLX Euro III	Kolkata	43206	Manual	First	1086.00	63	5.00	4.55	0.99	Hyundai	13	0	0	0	0	1	72
1271	Hyundai Santro GLS I - Euro II	Jaipur	89000	Manual	Second	999.00	63	5.00	4.55	1.60	Hyundai	11	0	0	0	0	1	14
1319	Fiat Petra 1.2 EL	Pune	120000	Manual	Second	1242.00	72	5.00	9.07	0.85	Fiat	15	0	0	0	0	1	205
1345	Maruti Baleno Vxi	Pune	70000	Manual	First	1590.00	83.10	5.00	8.80	1.30	Maruti	15	0	0	0	0	1	14
1388	Maruti Swift VDI BSIV	Pune	37000	Manual	First	1248.00	87	5.00	7.88	4.85	Maruti	7	0	1	0	0	0	49
1419	Hyundai Santro Xing XL	Chennai	82000	Manual	Second	1086.00	63	5.00	4.55	1.35	Hyundai	13	0	0	0	0	1	14
1555	Chevrolet Optra 1.6 Elite	Pune	150000	Manual	First	1598.00	157.80	5.00	11.30	1.50	Chevrolet	15	0	0	0	0	1	129
1578	Mahindra Bolero ZLX	Chennai	35000	Manual	First	2523.00	70	7.00	11.01	7.00	Mahindra	8	0	1	0	0	0	221
1649	Toyota Etios Liva GD	Pune	32000	Manual	First	1364.00	67.05	5.00	8.53	3.85	Toyota	9	0	1	0	0	0	132
1672	Nissan Micra Diesel	Jaipur	80000	Manual	Second	1461.00	65	5.00	9.47	2.10	Nissan	9	0	1	0	0	0	284
1857	Hyundai Santro DX	Hyderabad	96000	Manual	Second	999.00	63	5.00	4.55	2.20	Hyundai	13	0	0	0	0	1	14
1999	Mercedes-Benz E-Class 220 CDI	Pune	121000	Manual	First	2148.00	191.70	5.00	86.97	5.00	Mercedes-Benz	19	0	1	0	0	0	89
2053	Mahindra Jeep MM 550 PE	Hyderabad	26000	Manual	First	2498.00	105	6.00	18.86	6.99	Mahindra	11	0	1	0	0	0	14
2130	Hyundai Santro GLS I - Euro II	Coimbatore	51019	Manual	First	999.00	63	5.00	4.55	3.48	Hyundai	8	0	0	0	0	1	14
2164	Maruti Swift VDI BSIV W ABS	Hyderabad	86514	Manual	First	1248.00	87	5.00	7.88	4.25	Maruti	10	0	1	0	0	0	49
2262	Toyota Etios GD	Bangalore	58253	Manual	Second	1364.00	67.05	5.00	8.53	4.45	Toyota	8	0	1	0	0	0	132
2267	Toyota Qualis RS E2	Pune	215750	Manual	Second	2446.00	71.02	10.00	24.01	3.50	Toyota	16	0	1	0	0	0	14
2305	Porsche Cayman 2009-2012 S tiptronic	Mumbai	8000	Automatic	First	3436.00	320	2.00	136.00	43.00	Porsche	9	0	0	0	0	1	266
2343	Hyundai Santro AT	Hyderabad	74483	Automatic	First	999.00	63	5.00	4.55	2.30	Hyundai	14	0	0	0	0	1	14
2369	Maruti Estilo LXI	Chennai	56000	Manual	Second	1061.00	64	NaN	7.88	1.50	Maruti	12	0	0	0	0	1	284
2393	Toyota Etios GD	Chennai	150000	Manual	Third	1364.00	67.05	5.00	8.53	3.00	Toyota	8	0	1	0	0	0	132
2441	Toyota Etios Liva G	Kolkata	42118	Manual	First	1197.00	67.05	5.00	8.53	2.19	Toyota	9	0	0	0	0	1	249
2450	Tata Indica DLS	Kolkata	75648	Manual	First	1405.00	64.40	5.00	7.63	0.82	Tata	11	0	1	0	0	0	15
2497	Maruti Swift VDI BSIV	Hyderabad	68800	Manual	First	1248.00	87	5.00	7.88	5.00	Maruti	7	0	1	0	0	0	49
2501	Maruti Swift VDI BSIV	Hyderabad	51787	Manual	First	1248.00	87	5.00	7.88	6.45	Maruti	4	0	1	0	0	0	49
2527	Hyundai Santro Xing XO CNG	Pune	100000	Manual	Third	1086.00	63	5.00	4.55	1.20	Hyundai	15	1	0	0	0	0	72
2579	Hyundai Santro Xing XG eRLX Euro III	Mumbai	50888	Manual	First	1086.00	63	5.00	4.55	1.15	Hyundai	14	0	0	0	0	1	72
2597	Hyundai Santro Xing XP	Pune	70000	Manual	First	1086.00	63	5.00	4.55	1.12	Hyundai	13	0	0	0	0	1	14
2635	Hyundai Santro Xing XG eRLX Euro III	Kochi	58815	Manual	First	1086.00	63	5.00	4.55	1.99	Hyundai	13	0	0	0	0	1	72
2640	Maruti Swift VDI BSIV	Pune	121000	Manual	Second	1248.00	87	5.00	7.88	2.35	Maruti	13	0	1	0	0	0	49
2891	Maruti Swift VDI BSIV	Bangalore	58291	Manual	First	1248.00	87	5.00	7.88	5.00	Maruti	7	0	1	0	0	0	49
3033	Hyundai Santro Xing XP	Jaipur	120000	Manual	First	1086.00	63	5.00	4.55	1.15	Hyundai	15	0	0	0	0	1	14
3061	Hyundai Santro GS	Ahmedabad	58000	Manual	Second	999.00	63	5.00	4.55	1.51	Hyundai	15	0	0	0	0	1	14
3104	Maruti Swift VDI BSIV	Kolkata	41300	Manual	First	1248.00	87	5.00	7.88	3.38	Maruti	7	0	1	0	0	0	49
3189	Hyundai Santro GS zipDrive - Euro II	Chennai	67000	Manual	Third	999.00	63	5.00	4.55	1.20	Hyundai	18	0	0	0	0	1	14
3247	Toyota Etios GD	Chennai	79000	Manual	First	1364.00	67.05	5.00	8.53	4.67	Toyota	9	0	1	0	0	0	132
3290	Ford Fiesta 1.4 SXi TDCi	Hyderabad	83000	Manual	Second	1399.00	68	5.00	11.69	2.40	Ford	12	0	1	0	0	0	49
3439	Maruti Swift VDI BSIV W ABS	Pune	63600	Manual	Second	1248.00	87	5.00	7.88	3.10	Maruti	10	0	1	0	0	0	49
3516	Hyundai Santro GLS I - Euro I	Pune	65400	Manual	First	1086.00	63	5.00	4.55	2.10	Hyundai	9	0	0	0	0	1	14
3533	Nissan Teana 230jM	Mumbai	86340	Automatic	Second	2349.00	170	5.00	11.33	3.25	Nissan	12	0	0	0	0	1	389
3589	Tata Indica DLS	Hyderabad	100000	Manual	Second	1405.00	64.40	5.00	7.63	1.30	Tata	15	0	1	0	0	0	15
3628	Maruti Swift VDI BSIV W ABS	Coimbatore	70478	Manual	First	1248.00	87	5.00	7.88	6.86	Maruti	4	0	1	0	0	0	49
3638	Honda CR-V Sport	Hyderabad	67758	Manual	First	2354.00	145	5.00	33.51	9.50	Honda	10	0	0	0	0	1	88
3645	Hyundai Santro Xing XP	Bangalore	167000	Manual	First	1086.00	63	5.00	4.55	1.35	Hyundai	16	0	0	0	0	1	14
3669	Hyundai Santro Xing XO	Hyderabad	57309	Manual	First	1086.00	63	5.00	4.55	2.10	Hyundai	13	0	0	0	0	1	72
3733	Maruti Esteem Vxi	Delhi	69496	Manual	First	1298.00	122	5.00	7.88	1.10	Maruti	15	0	0	0	0	1	231
3800	Ford Endeavour Hurricane LE	Mumbai	129000	Automatic	First	2953.00	156	NaN	35.29	7.00	Ford	8	0	1	0	0	0	27
3882	Maruti Estilo LXI	Kolkata	40000	Manual	Second	1061.00	64	NaN	7.88	2.50	Maruti	10	0	0	0	0	1	284
3898	Toyota Etios GD	Coimbatore	46496	Manual	First	1364.00	67.05	5.00	8.53	5.30	Toyota	7	0	1	0	0	0	132
3930	Maruti Swift VDI BSIV W ABS	Bangalore	62588	Manual	First	1248.00	87	5.00	7.88	5.95	Maruti	5	0	1	0	0	0	49
3999	Audi A4 3.2 FSI Tiptronic Quattro	Hyderabad	125000	Automatic	First	3197.00	252	0.00	53.14	18.00	Audi	8	0	0	0	0	1	226
4077	Maruti Baleno LXI - BSIII	Kochi	70362	Manual	Second	1590.00	83.10	5.00	8.80	1.81	Maruti	14	0	0	0	0	1	125
4080	Toyota Etios Liva GD	Mumbai	74000	Manual	First	1364.00	67.05	5.00	8.53	3.60	Toyota	9	0	1	0	0	0	132
4351	Maruti Esteem LX BSII	Jaipur	55000	Manual	Third	1298.00	122	5.00	7.88	0.70	Maruti	17	0	0	0	0	1	231
4354	Toyota Etios Liva GD	Delhi	72351	Manual	First	1364.00	67.05	5.00	8.53	2.65	Toyota	8	0	1	0	0	0	132
4629	Fiat Siena 1.2 ELX	Jaipur	70000	Manual	Third	1242.00	99	5.00	9.07	0.55	Fiat	19	0	0	0	0	1	14
4709	Maruti 1000 AC	Hyderabad	104000	Manual	Second	970.00	46	5.00	7.88	0.85	Maruti	22	0	0	0	0	1	89
4714	Mercedes-Benz E-Class 220 CDI	Ahmedabad	78000	Manual	Second	2148.00	191.70	5.00	86.97	14.50	Mercedes-Benz	9	0	1	0	0	0	89
4744	Hyundai Santro Xing XL eRLX Euro III	Kolkata	45000	Manual	First	1086.00	63	5.00	4.55	0.95	Hyundai	15	0	0	0	0	1	72
4830	Maruti Esteem Vxi	Chennai	95000	Manual	First	1298.00	122	5.00	7.88	0.80	Maruti	16	0	0	0	0	1	231
4886	Hyundai Santro Xing XL AT eRLX Euro II	Hyderabad	76000	Automatic	First	1086.00	63	5.00	4.55	2.40	Hyundai	13	0	0	0	0	1	72
4900	Hyundai Santro Xing XL AT eRLX Euro III	Hyderabad	71000	Automatic	First	1086.00	63	5.00	4.55	2.40	Hyundai	13	0	0	0	0	1	72
4954	Toyota Etios Liva GD	Coimbatore	62601	Manual	First	1364.00	67.05	5.00	8.53	4.67	Toyota	7	0	1	0	0	0	132
5065	Maruti Swift VDI BSIV W ABS	Hyderabad	64000	Manual	First	1248.00	87	5.00	7.88	5.99	Maruti	4	0	1	0	0	0	49
5119	Hyundai Santro Xing XP	Kolkata	45500	Manual	Second	1086.00	63	5.00	4.55	1.17	Hyundai	12	0	0	0	0	1	14
5228	Hyundai Santro Xing XL eRLX Euro III	Pune	126000	Manual	Second	1086.00	63	5.00	4.55	1.20	Hyundai	15	0	0	0	0	1	72
5426	Hyundai Santro Xing XL	Chennai	85000	Manual	Third	1086.00	63	5.00	4.55	1.30	Hyundai	14	0	0	0	0	1	14
5438	Ford Endeavour XLT TDCi 4X4	Mumbai	116000	Manual	Third	2499.00	156	7.00	35.29	3.75	Ford	13	0	1	0	0	0	330
5458	Maruti Swift VDI BSIV	Hyderabad	81609	Manual	First	1248.00	87	5.00	7.88	5.55	Maruti	6	0	1	0	0	0	49
5529	Hyundai Santro LP - Euro II	Chennai	105000	Manual	First	999.00	63	5.00	4.55	1.75	Hyundai	15	0	0	0	0	1	14
5533	Hyundai Santro Xing XO eRLX Euro II	Pune	72000	Manual	Second	1086.00	63	5.00	4.55	1.11	Hyundai	13	0	0	0	0	1	72
5647	Toyota Qualis Fleet A3	Mumbai	227000	Manual	Fourth & Above	2446.00	71.02	8.00	24.01	2.20	Toyota	19	0	1	0	0	0	14
5755	Hyundai Santro Xing XL eRLX Euro III	Ahmedabad	51000	Manual	First	1086.00	63	5.00	4.55	1.75	Hyundai	12	0	0	0	0	1	72
5759	Hyundai Santro Xing XL eRLX Euro III	Kolkata	46707	Manual	First	1086.00	63	5.00	4.55	0.99	Hyundai	13	0	0	0	0	1	72
5861	Hyundai Santro Xing XO	Chennai	79000	Manual	First	1086.00	63	5.00	4.55	1.85	Hyundai	13	0	0	0	0	1	72
5873	Hyundai Santro Xing XO eRLX Euro II	Pune	47200	Manual	Second	1086.00	63	5.00	4.55	1.20	Hyundai	14	0	0	0	0	1	72
5893	Maruti Estilo LXI	Chennai	51000	Manual	Second	1061.00	64	NaN	7.88	1.75	Maruti	12	0	0	0	0	1	284
5925	Skoda Laura Classic 1.8 TSI	Pune	85000	Manual	First	1798.00	157.80	5.00	25.83	2.85	Skoda	10	0	0	0	0	1	26
5943	Mahindra Jeep MM 540 DP	Chennai	75000	Manual	First	2112.00	62	6.00	18.86	1.70	Mahindra	18	0	1	0	0	0	14
5985	Toyota Etios GD	Delhi	70000	Manual	First	1364.00	67.05	5.00	8.53	3.88	Toyota	7	0	1	0	0	0	132
6022	Toyota Etios Liva GD	Hyderabad	139000	Manual	First	1364.00	67.05	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6093	Hyundai Santro Xing XL	Bangalore	47000	Manual	Second	1086.00	63	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	14
6141	Toyota Etios Liva GD	Jaipur	121134	Manual	First	1364.00	67.05	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6162	Ford Fiesta 1.4 SXI Duratorq	Jaipur	135000	Manual	First	1399.00	68	5.00	11.69	NaN	Ford	12	0	1	0	0	0	49
6210	Tata Indica DLS	Chennai	140000	Manual	Second	1405.00	64.40	5.00	7.63	NaN	Tata	14	0	1	0	0	0	15
6216	Hindustan Motors Contessa 2.0 DSL	Pune	65000	Manual	Second	1995.00	52	5.00	11.30	NaN	Hindustan	24	0	1	0	0	0	233
6278	Toyota Etios GD	Bangalore	90000	Manual	First	1364.00	67.05	5.00	8.53	NaN	Toyota	8	0	1	0	0	0	132
6386	Nissan Teana 230jM	Coimbatore	67038	Automatic	First	2349.00	170	5.00	11.33	NaN	Nissan	11	0	0	0	0	1	389
6428	Nissan Teana 230jM	Chennai	63288	Automatic	First	2349.00	170	5.00	11.33	NaN	Nissan	12	0	0	0	0	1	389
6439	Hyundai Santro GLS I - Euro II	Bangalore	43189	Manual	First	999.00	63	5.00	4.55	NaN	Hyundai	9	0	0	0	0	1	14
6445	Maruti Swift VDI BSIV W ABS	Coimbatore	69564	Manual	First	1248.00	87	5.00	7.88	NaN	Maruti	4	0	1	0	0	0	49
6454	Hyundai Santro LS zipDrive Euro I	Chennai	120000	Manual	Fourth & Above	999.00	63	5.00	4.55	NaN	Hyundai	18	0	0	0	0	1	14
6576	Hyundai Santro LS zipPlus	Kolkata	80000	Manual	First	1086.00	63	5.00	4.55	NaN	Hyundai	18	0	0	0	0	1	14
6591	Ford Fiesta 1.4 SXi TDCi	Pune	134000	Manual	First	1399.00	68	5.00	11.69	NaN	Ford	11	0	1	0	0	0	49
6625	Maruti Swift VDI BSIV	Kolkata	72000	Manual	First	1248.00	87	5.00	7.88	NaN	Maruti	8	0	1	0	0	0	49
6697	Hyundai Santro Xing XL	Jaipur	85000	Manual	Second	1086.00	63	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	14
6723	Ford Fiesta 1.4 SXi TDCi	Kolkata	39408	Manual	First	1399.00	68	5.00	11.69	NaN	Ford	11	0	1	0	0	0	49
6891	Toyota Etios GD	Kolkata	60000	Manual	First	1364.00	67.05	5.00	8.53	NaN	Toyota	7	0	1	0	0	0	132
6896	Toyota Etios Liva GD	Hyderabad	86000	Manual	First	1364.00	67.05	5.00	8.53	NaN	Toyota	7	0	1	0	0	0	132
6947	Toyota Etios Liva G	Delhi	77800	Manual	First	1197.00	67.05	5.00	8.53	NaN	Toyota	8	0	0	0	0	1	249
7092	Hyundai Santro Xing XG AT eRLX Euro III	Coimbatore	64168	Automatic	First	1086.00	63	5.00	4.55	NaN	Hyundai	13	0	0	0	0	1	72
7145	Toyota Etios Liva G	Kolkata	37212	Manual	First	1197.00	67.05	5.00	8.53	NaN	Toyota	8	0	0	0	0	1	249
194	Honda City 1.5 GXI	Ahmedabad	60006	Manual	First	1500.00	100	NaN	13.58	2.95	Honda	13	0	0	0	0	1	14
208	Maruti Swift 1.3 VXi	Kolkata	42001	Manual	First	1300.00	87	NaN	7.88	2.11	Maruti	10	0	0	0	0	1	133
733	Maruti Swift 1.3 VXi	Chennai	97800	Manual	Third	1300.00	87	NaN	7.88	1.75	Maruti	14	0	0	0	0	1	133
749	Land Rover Range Rover 3.0 D	Mumbai	55001	Automatic	Second	3000.00	345.98	NaN	120.00	26.50	Land Rover	12	0	1	0	0	0	14
1294	Honda City 1.3 DX	Delhi	55005	Manual	First	1300.00	84	NaN	13.58	3.20	Honda	11	0	0	0	0	1	27
1327	Maruti Swift 1.3 ZXI	Hyderabad	50295	Manual	First	1300.00	87	NaN	7.88	5.80	Maruti	5	0	0	0	0	1	133
1385	Honda City 1.5 GXI	Pune	115000	Manual	Second	1500.00	100	NaN	13.58	1.50	Honda	16	0	0	0	0	1	14
2074	Maruti Swift 1.3 LXI	Pune	24255	Manual	First	1300.00	87	NaN	7.88	3.15	Maruti	9	0	0	0	0	1	133
2325	Maruti Swift 1.3 VXI ABS	Pune	67000	Manual	First	1300.00	87	NaN	7.88	4.70	Maruti	5	0	0	0	0	1	133
2335	Maruti Swift 1.3 VXi	Mumbai	55000	Manual	Second	1300.00	87	NaN	7.88	1.75	Maruti	13	0	0	0	0	1	133
2668	Maruti Swift 1.3 VXi	Kolkata	32986	Manual	First	1300.00	87	NaN	7.88	4.24	Maruti	6	0	0	0	0	1	133
3404	Maruti Swift 1.3 VXi	Jaipur	125000	Manual	Fourth & Above	1300.00	87	NaN	7.88	2.35	Maruti	14	0	0	0	0	1	133
4011	Fiat Punto 1.3 Emotion	Pune	45271	Manual	First	1300.00	91.70	NaN	8.60	2.60	Fiat	9	0	1	0	0	0	285
4152	Land Rover Range Rover 3.0 D	Mumbai	75000	Automatic	Second	3000.00	345.98	NaN	120.00	16.11	Land Rover	17	0	1	0	0	0	14
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	Manual	First	1200.00	67	NaN	8.60	3.93	Fiat	3	0	0	0	0	1	328
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	Manual	First	1400.00	88.80	NaN	8.60	1.49	Fiat	10	0	0	0	0	1	209
5015	Maruti Swift 1.3 VXi	Delhi	63000	Manual	First	1300.00	87	NaN	7.88	1.60	Maruti	14	0	0	0	0	1	133
5185	Maruti Swift 1.3 LXI	Delhi	52000	Manual	First	1300.00	87	NaN	7.88	3.65	Maruti	8	0	0	0	0	1	133
5270	Honda City 1.5 GXI	Bangalore	53000	Manual	Second	1500.00	100	NaN	13.58	1.85	Honda	18	0	0	0	0	1	14
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	1800.00	157.80	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	1400.00	88.73	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	1300.00	87	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	1400.00	88.80	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	1300.00	87	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133

#drop nan power
usedcarworks_df = usedcarworks_df.dropna(subset=['Power'])

#save to original dataframe
usedcarworks_df = usedcarworks_df.append(powernull_df)
usedcarworks_df

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage
0	Maruti Wagon R LXI CNG	Mumbai	72000	Manual	First	998.00	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	Manual	First	1582.00	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1
2	Honda Jazz V	Chennai	46000	Manual	First	1199.00	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2
3	Maruti Ertiga VDI	Chennai	87000	Manual	First	1248.00	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Automatic	Second	1968.00	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Manual	Second	1800.00	157.80	NaN	25.83	NaN	Skoda	11	0	0	0	0	1	26
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Automatic	Second	1400.00	88.73	NaN	10.25	NaN	Hyundai	8	0	0	0	0	1	89
6651	Maruti Swift 1.3 VXi	Kolkata	36009	Manual	First	1300.00	87	NaN	7.88	NaN	Maruti	5	0	0	0	0	1	133
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Manual	Third	1400.00	88.80	NaN	8.60	NaN	Fiat	10	0	0	0	0	1	209
6685	Maruti Swift 1.3 VXi	Pune	115000	Manual	Second	1300.00	87	NaN	7.88	NaN	Maruti	10	0	0	0	0	1	133

7252 rows × 18 columns

We don't have any more missing values for Power

# sorting by amount of missing values
usedcarworks_df.isnull().sum().sort_values(ascending=False)

Price                 1234
Seats                   53
Brand                    0
Fuel_Type_Petrol         0
Fuel_Type_LPG            0
Fuel_Type_Electric       0
Fuel_Type_Diesel         0
Fuel_Type_CNG            0
Age                      0
Name                     0
Location                 0
New_Price                0
Power                    0
Engine                   0
Owner_Type               0
Transmission             0
Kilometers_Driven        0
Mileage                  0
dtype: int64

Summarizing we see lots of Price and Seats with empty values

Seats

#lets see seats
usedcarworks_df['Seats']

0      5.00
1      5.00
2      5.00
3      7.00
4      5.00
       ... 
6042    NaN
6544    NaN
6651    NaN
6677    NaN
6685    NaN
Name: Seats, Length: 7252, dtype: float64

#how many 'Seats' rows are NaN
usedcarworks_df['Seats'].isna().value_counts()

False    7199
True       53
Name: Seats, dtype: int64

#from a general look, theese are mostly small passenger cars
#lets fill these values w/ the MODE
seatsmode= usedcarworks_df['Seats'].mode()
seatsmode

0   5.00
Name: Seats, dtype: float64

#lets replace empty values with mode
usedcarworks_df['Seats'] = usedcarworks_df['Seats'].fillna(usedcarworks_df['Seats'].mode()[0])

#lets check how many nan values are left for seats
usedcarworks_df.loc[usedcarworks_df['Seats'].isna()]

	Name	Location	Kilometers_Driven	Transmission	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage

No more nan seats.

Transmission

Transmision is important and affects the price of a car. We can make use dummy variables and drop the first column.

#Transmission
#How many different type of Transmissions?
usedcarworks_df['Transmission'].value_counts()

Manual       5204
Automatic    2048
Name: Transmission, dtype: int64

#make it dummies, drop the first column
usedcarworks_df = pd.get_dummies(usedcarworks_df, columns=['Transmission'], drop_first=True)

usedcarworks_df

	Name	Location	Kilometers_Driven	Owner_Type	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual
0	Maruti Wagon R LXI CNG	Mumbai	72000	First	998.00	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0	1
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	First	1582.00	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1	1
2	Honda Jazz V	Chennai	46000	First	1199.00	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2	1
3	Maruti Ertiga VDI	Chennai	87000	First	1248.00	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3	1
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	Second	1968.00	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	Second	1800.00	157.80	5.00	25.83	NaN	Skoda	11	0	0	0	0	1	26	1
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	Second	1400.00	88.73	5.00	10.25	NaN	Hyundai	8	0	0	0	0	1	89	0
6651	Maruti Swift 1.3 VXi	Kolkata	36009	First	1300.00	87	5.00	7.88	NaN	Maruti	5	0	0	0	0	1	133	1
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	Third	1400.00	88.80	5.00	8.60	NaN	Fiat	10	0	0	0	0	1	209	1
6685	Maruti Swift 1.3 VXi	Pune	115000	Second	1300.00	87	5.00	7.88	NaN	Maruti	10	0	0	0	0	1	133	1

7252 rows × 18 columns

Ownership

#Owner Type
#How many different type of owners
usedcarworks_df['Owner_Type'].value_counts()

First             5951
Second            1152
Third              137
Fourth & Above      12
Name: Owner_Type, dtype: int64

#we make it dummies but don't drop any columns
usedcarworks_df = pd.get_dummies(usedcarworks_df, columns=['Owner_Type'])
usedcarworks_df

	Name	Location	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third
0	Maruti Wagon R LXI CNG	Mumbai	72000	998.00	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0	1	1	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	1582.00	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1	1	1	0	0	0
2	Honda Jazz V	Chennai	46000	1199.00	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2	1	1	0	0	0
3	Maruti Ertiga VDI	Chennai	87000	1248.00	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3	1	1	0	0	0
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	1968.00	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6042	Skoda Laura 1.8 TSI Ambition	Bangalore	72000	1800.00	157.80	5.00	25.83	NaN	Skoda	11	0	0	0	0	1	26	1	0	0	1	0
6544	Hyundai i20 new Sportz AT 1.4	Bangalore	58000	1400.00	88.73	5.00	10.25	NaN	Hyundai	8	0	0	0	0	1	89	0	0	0	1	0
6651	Maruti Swift 1.3 VXi	Kolkata	36009	1300.00	87	5.00	7.88	NaN	Maruti	5	0	0	0	0	1	133	1	1	0	0	0
6677	Fiat Punto 1.4 Emotion	Jaipur	65000	1400.00	88.80	5.00	8.60	NaN	Fiat	10	0	0	0	0	1	209	1	0	0	0	1
6685	Maruti Swift 1.3 VXi	Pune	115000	1300.00	87	5.00	7.88	NaN	Maruti	10	0	0	0	0	1	133	1	0	0	1	0

7252 rows × 21 columns

New Price

There are no nan entries but the price of car seems very high.italicized text

usedcarworks_df.sort_values(by=['New_Price'], ascending = False)

	Name	Location	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third
6354	Bentley Flying Spur W12	Bangalore	31200	5998.00	616 bhp	5.00	375.00	NaN	Bentley	12	0	0	0	0	1	273	0	0	0	1	0
5521	Bentley Continental Flying Spur	Hyderabad	48000	5998.00	552 bhp	4.00	375.00	59.00	Bentley	14	0	0	0	0	1	278	0	1	0	0	0
4079	Land Rover Range Rover 3.0 Diesel LWB Vogue	Hyderabad	25000	2993.00	255 bhp	5.00	230.00	160.00	Land Rover	3	0	1	0	0	0	377	0	1	0	0	0
4674	Mercedes-Benz S-Class 280 AT	Kochi	119859	2987.00	210 bhp	5.00	171.00	7.10	Mercedes-Benz	16	0	0	0	0	1	73	0	1	0	0	0
4245	Mercedes-Benz S-Class 320 L	Coimbatore	84143	2987.00	210 bhp	5.00	171.00	17.09	Mercedes-Benz	13	0	0	0	0	1	73	1	1	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3080	Renault KWID RXL	Kochi	24214	799.00	53.3 bhp	5.00	3.95	3.46	Renault	3	0	0	0	0	1	52	1	0	0	1	0
6804	Renault KWID RXL	Kochi	16292	799.00	53.3 bhp	5.00	3.95	NaN	Renault	2	0	0	0	0	1	52	1	1	0	0	0
4221	Maruti Alto K10 LXI	Kochi	51349	998.00	67.1 bhp	5.00	3.91	2.93	Maruti	6	0	0	0	0	1	96	1	1	0	0	0
6314	Maruti Alto K10 LXI	Kochi	42954	998.00	67.1 bhp	5.00	3.91	NaN	Maruti	3	0	0	0	0	1	96	1	1	0	0	0
4571	Maruti Alto K10 LXI	Delhi	35027	998.00	67.1 bhp	5.00	3.91	3.00	Maruti	4	0	0	0	0	1	96	1	1	0	0	0

7252 rows × 21 columns

We checked in https://www.cardekho.com/carmodels/Bentley/Bentley_Flying_Spur that the Bentley Continental Flying Spur sells somewhere in between 322 - 341.

The Land Rover Range Rover 3.0 Diesel LWB Vogue sells frrom 239 to 417

So we conclude that the data for Prices is OK.

Price

Price nan is an issue. Too many nan Prices

We have a few choices:

1. Drop all cars with Price nan Easy to achieve. But is it advisable? How would it affect our dataset?

2. Complete cars w/o Price with mean This is very difficult because there may be low end cars and high end cars.

3. Categorize cars w/o Price according to Year, Model This may be complex.

#sort null prices to visualize
pricenull_df= pd.DataFrame(usedcarworks_df.loc[usedcarworks_df['Price'].isna()])
pricenull_df.sort_values(by=['Name'],ascending=False)

	Name	Location	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third
6384	Volvo XC90 2007-2015 D5 AWD	Bangalore	70000	2400.00	200 bhp	7.00	45.67	NaN	Volvo	4	0	1	0	0	0	303	1	1	0	0	0
6659	Volvo XC90 2007-2015 D5 AWD	Delhi	22230	2400.00	200 bhp	7.00	45.67	NaN	Volvo	6	0	1	0	0	0	303	1	1	0	0	0
6681	Volvo XC60 D4 SUMMUM	Ahmedabad	64000	1984.00	181 bhp	5.00	45.67	NaN	Volvo	7	0	1	0	0	0	129	0	1	0	0	0
6859	Volvo V40 Cross Country D3	Chennai	60000	1984.00	150 bhp	5.00	37.90	NaN	Volvo	5	0	1	0	0	0	86	0	1	0	0	0
6809	Volvo S80 D5	Delhi	87000	2400.00	215 bhp	5.00	45.67	NaN	Volvo	9	0	1	0	0	0	151	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
7168	Audi A4 2.0 TDI	Bangalore	65475	1968.00	147.51 bhp	5.00	53.14	NaN	Audi	6	0	1	0	0	0	46	0	1	0	0	0
7238	Audi A4 2.0 TDI	Hyderabad	64000	1968.00	147.51 bhp	5.00	53.14	NaN	Audi	9	0	1	0	0	0	46	0	1	0	0	0
7233	Audi A4 2.0 TDI	Bangalore	21143	1968.00	147.51 bhp	5.00	53.14	NaN	Audi	6	0	1	0	0	0	46	0	1	0	0	0
6742	Audi A3 35 TDI Premium Plus	Hyderabad	52300	1968.00	143 bhp	5.00	42.89	NaN	Audi	5	0	1	0	0	0	187	0	1	0	0	0
6663	Audi A3 35 TDI Attraction	Ahmedabad	84004	1968.00	143 bhp	5.00	42.89	NaN	Audi	6	0	1	0	0	0	187	0	1	0	0	0

1234 rows × 21 columns

#lets sort by car name
pricenull_df['Name'].value_counts()

Maruti Alto LXi                           9
Honda City 1.5 V MT                       8
Maruti Swift Dzire VDI                    8
Volkswagen Polo 1.2 MPI Highline          8
Audi A6 2011-2015 2.0 TDI Premium Plus    7
                                         ..
Hyundai i20 Magna Optional 1.2            1
Nissan Micra XL CVT                       1
Hyundai i20 Sportz AT 1.4                 1
Hyundai Verna 1.4 CX                      1
Fiat Punto 1.4 Emotion                    1
Name: Name, Length: 768, dtype: int64

#lets focus on the first car, the Maruti Alto Lxi with 9 nan Price entries
pricenull_df.loc[pricenull_df['Name']=='Maruti Alto LXi']

	Name	Location	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third
6192	Maruti Alto LXi	Coimbatore	64631	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	8	0	0	0	0	1	38	1	1	0	0	0
6280	Maruti Alto LXi	Jaipur	40000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	7	0	0	0	0	1	38	1	0	0	1	0
6684	Maruti Alto LXi	Chennai	60000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	9	0	0	0	0	1	38	1	1	0	0	0
6746	Maruti Alto LXi	Chennai	94385	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	10	0	0	0	0	1	38	1	0	0	1	0
6803	Maruti Alto LXi	Jaipur	65000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	12	0	0	0	0	1	38	1	0	0	1	0
6838	Maruti Alto LXi	Jaipur	90000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	9	0	0	0	0	1	38	1	1	0	0	0
7137	Maruti Alto LXi	Kolkata	39008	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	12	0	0	0	0	1	38	1	1	0	0	0
7165	Maruti Alto LXi	Hyderabad	62659	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	11	0	0	0	0	1	38	1	1	0	0	0
7193	Maruti Alto LXi	Jaipur	75000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	14	0	0	0	0	1	38	1	1	0	0	0

From here we start seeing the complexity of trying to assign a price to these entries. Look they have different ages, owner type are not the same, odometer reading is different, and locations are different.

Would it make sense to approximate the price to other Maruti Alto LXi that have a Price value?

#How many Maruti Alto LXi are in the dataset?
MarutiAltoLXi = usedcarworks_df.loc[usedcarworks_df['Name']=='Maruti Alto LXi'].value_counts()
MarutiAltoLXi

Name             Location    Kilometers_Driven  Engine  Power     Seats  New_Price  Price  Brand   Age  Fuel_Type_CNG  Fuel_Type_Diesel  Fuel_Type_Electric  Fuel_Type_LPG  Fuel_Type_Petrol  Mileage  Transmission_Manual  Owner_Type_First  Owner_Type_Fourth & Above  Owner_Type_Second  Owner_Type_Third
Maruti Alto LXi  Ahmedabad   35000              796.00  46.3 bhp  5.00   4.36       1.50   Maruti  11   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                 Hyderabad   76976              796.00  46.3 bhp  5.00   4.36       2.15   Maruti  9    0              0                 0                   0              1                 38       1                    0                 0                          1                  0                   1
                 Mumbai      66000              796.00  46.3 bhp  5.00   4.36       1.50   Maruti  10   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             26299              796.00  46.3 bhp  5.00   4.36       1.20   Maruti  12   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                 Kolkata     42000              796.00  46.3 bhp  5.00   4.36       1.09   Maruti  12   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             38127              796.00  46.3 bhp  5.00   4.36       1.55   Maruti  10   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             38000              796.00  46.3 bhp  5.00   4.36       1.10   Maruti  12   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             18700              796.00  46.3 bhp  5.00   4.36       1.25   Maruti  13   0              0                 0                   0              1                 38       1                    0                 0                          1                  0                   1
                 Jaipur      100000             796.00  46.3 bhp  5.00   4.36       1.50   Maruti  9    0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             63000              796.00  46.3 bhp  5.00   4.36       1.50   Maruti  11   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                 Hyderabad   76161              796.00  46.3 bhp  5.00   4.36       1.65   Maruti  13   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                 Chennai     56000              796.00  46.3 bhp  5.00   4.36       1.40   Maruti  14   0              0                 0                   0              1                 38       1                    0                 0                          1                  0                   1
                 Hyderabad   65090              796.00  46.3 bhp  5.00   4.36       2.15   Maruti  11   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             52195              796.00  46.3 bhp  5.00   4.36       1.75   Maruti  13   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                 Delhi       90000              796.00  46.3 bhp  5.00   4.36       1.25   Maruti  12   0              0                 0                   0              1                 38       1                    0                 0                          1                  0                   1
                 Coimbatore  49008              796.00  46.3 bhp  5.00   4.36       2.88   Maruti  8    0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             48105              796.00  46.3 bhp  5.00   4.36       3.10   Maruti  10   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                 Chennai     75000              796.00  46.3 bhp  5.00   4.36       2.10   Maruti  9    0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             68000              796.00  46.3 bhp  5.00   4.36       1.50   Maruti  14   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                             65000              796.00  46.3 bhp  5.00   4.36       1.59   Maruti  12   0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
                 Pune        38000              796.00  46.3 bhp  5.00   4.36       2.25   Maruti  8    0              0                 0                   0              1                 38       1                    1                 0                          0                  0                   1
dtype: int64

1st Option: Drop Price nan

#1st attempt: Drop price null
usedcarworksprice_df = usedcarworks_df.dropna(subset=['Price'])
usedcarworksprice_df

	Name	Location	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third
0	Maruti Wagon R LXI CNG	Mumbai	72000	998.00	58.16 bhp	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0	1	1	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	1582.00	126.2 bhp	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1	1	1	0	0	0
2	Honda Jazz V	Chennai	46000	1199.00	88.7 bhp	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2	1	1	0	0	0
3	Maruti Ertiga VDI	Chennai	87000	1248.00	88.76 bhp	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3	1	1	0	0	0
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	1968.00	140.8 bhp	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4697	Fiat Punto 1.2 Dynamic	Kochi	17941	1200.00	67	5.00	8.60	3.93	Fiat	3	0	0	0	0	1	328	1	1	0	0	0
4952	Fiat Punto 1.4 Emotion	Kolkata	47000	1400.00	88.80	5.00	8.60	1.49	Fiat	10	0	0	0	0	1	209	1	1	0	0	0
5015	Maruti Swift 1.3 VXi	Delhi	63000	1300.00	87	5.00	7.88	1.60	Maruti	14	0	0	0	0	1	133	1	1	0	0	0
5185	Maruti Swift 1.3 LXI	Delhi	52000	1300.00	87	5.00	7.88	3.65	Maruti	8	0	0	0	0	1	133	1	1	0	0	0
5270	Honda City 1.5 GXI	Bangalore	53000	1500.00	100	5.00	13.58	1.85	Honda	18	0	0	0	0	1	14	1	0	0	1	0

6018 rows × 21 columns

# sorting by amount of missing values
usedcarworksprice_df.isnull().sum().sort_values(ascending=False)

Name                         0
Fuel_Type_Diesel             0
Owner_Type_Second            0
Owner_Type_Fourth & Above    0
Owner_Type_First             0
Transmission_Manual          0
Mileage                      0
Fuel_Type_Petrol             0
Fuel_Type_LPG                0
Fuel_Type_Electric           0
Fuel_Type_CNG                0
Location                     0
Age                          0
Brand                        0
Price                        0
New_Price                    0
Seats                        0
Power                        0
Engine                       0
Kilometers_Driven            0
Owner_Type_Third             0
dtype: int64

2nd Option: Complete with mean values

#recall the following
pricenull_df['Name'].value_counts()

Maruti Alto LXi                           9
Honda City 1.5 V MT                       8
Maruti Swift Dzire VDI                    8
Volkswagen Polo 1.2 MPI Highline          8
Audi A6 2011-2015 2.0 TDI Premium Plus    7
                                         ..
Hyundai i20 Magna Optional 1.2            1
Nissan Micra XL CVT                       1
Hyundai i20 Sportz AT 1.4                 1
Hyundai Verna 1.4 CX                      1
Fiat Punto 1.4 Emotion                    1
Name: Name, Length: 768, dtype: int64

#Lets try first the cars with most missing entries
MarutiAltoLXi = usedcarworks_df.loc[usedcarworks_df['Name']=="Maruti Alto LXi"]
MarutiAltoLXi_mean = MarutiAltoLXi['Price'].mean()
MarutiAltoLXi_min = MarutiAltoLXi['Price'].min()
MarutiAltoLXi_max = MarutiAltoLXi['Price'].max()
MarutiAltoLXi_mode = MarutiAltoLXi['Price'].mode()
print ("The mean price is {:.2f}".format(MarutiAltoLXi_mean))
print ("The max price is {:.2f}".format(MarutiAltoLXi_min))
print ("The min price is {:.2f}".format(MarutiAltoLXi_max))
MarutiAltoLXi

The mean price is 1.71
The max price is 1.09
The min price is 3.10

	Name	Location	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third
40	Maruti Alto LXi	Delhi	90000	796.00	46.3 bhp	5.00	4.36	1.25	Maruti	12	0	0	0	0	1	38	1	0	0	1	0
335	Maruti Alto LXi	Ahmedabad	35000	796.00	46.3 bhp	5.00	4.36	1.50	Maruti	11	0	0	0	0	1	38	1	1	0	0	0
337	Maruti Alto LXi	Chennai	56000	796.00	46.3 bhp	5.00	4.36	1.40	Maruti	14	0	0	0	0	1	38	1	0	0	1	0
339	Maruti Alto LXi	Pune	38000	796.00	46.3 bhp	5.00	4.36	2.25	Maruti	8	0	0	0	0	1	38	1	1	0	0	0
1358	Maruti Alto LXi	Coimbatore	49008	796.00	46.3 bhp	5.00	4.36	2.88	Maruti	8	0	0	0	0	1	38	1	1	0	0	0
1401	Maruti Alto LXi	Coimbatore	48105	796.00	46.3 bhp	5.00	4.36	3.10	Maruti	10	0	0	0	0	1	38	1	1	0	0	0
1937	Maruti Alto LXi	Chennai	65000	796.00	46.3 bhp	5.00	4.36	1.59	Maruti	12	0	0	0	0	1	38	1	1	0	0	0
2051	Maruti Alto LXi	Chennai	75000	796.00	46.3 bhp	5.00	4.36	2.10	Maruti	9	0	0	0	0	1	38	1	1	0	0	0
2116	Maruti Alto LXi	Hyderabad	76976	796.00	46.3 bhp	5.00	4.36	2.15	Maruti	9	0	0	0	0	1	38	1	0	0	1	0
2181	Maruti Alto LXi	Mumbai	26299	796.00	46.3 bhp	5.00	4.36	1.20	Maruti	12	0	0	0	0	1	38	1	1	0	0	0
2481	Maruti Alto LXi	Hyderabad	76161	796.00	46.3 bhp	5.00	4.36	1.65	Maruti	13	0	0	0	0	1	38	1	1	0	0	0
2781	Maruti Alto LXi	Kolkata	38127	796.00	46.3 bhp	5.00	4.36	1.55	Maruti	10	0	0	0	0	1	38	1	1	0	0	0
3276	Maruti Alto LXi	Jaipur	63000	796.00	46.3 bhp	5.00	4.36	1.50	Maruti	11	0	0	0	0	1	38	1	1	0	0	0
3623	Maruti Alto LXi	Hyderabad	52195	796.00	46.3 bhp	5.00	4.36	1.75	Maruti	13	0	0	0	0	1	38	1	1	0	0	0
3750	Maruti Alto LXi	Hyderabad	65090	796.00	46.3 bhp	5.00	4.36	2.15	Maruti	11	0	0	0	0	1	38	1	1	0	0	0
3872	Maruti Alto LXi	Kolkata	38000	796.00	46.3 bhp	5.00	4.36	1.10	Maruti	12	0	0	0	0	1	38	1	1	0	0	0
4012	Maruti Alto LXi	Chennai	68000	796.00	46.3 bhp	5.00	4.36	1.50	Maruti	14	0	0	0	0	1	38	1	1	0	0	0
4257	Maruti Alto LXi	Mumbai	66000	796.00	46.3 bhp	5.00	4.36	1.50	Maruti	10	0	0	0	0	1	38	1	1	0	0	0
4953	Maruti Alto LXi	Jaipur	100000	796.00	46.3 bhp	5.00	4.36	1.50	Maruti	9	0	0	0	0	1	38	1	1	0	0	0
5130	Maruti Alto LXi	Kolkata	42000	796.00	46.3 bhp	5.00	4.36	1.09	Maruti	12	0	0	0	0	1	38	1	1	0	0	0
5712	Maruti Alto LXi	Kolkata	18700	796.00	46.3 bhp	5.00	4.36	1.25	Maruti	13	0	0	0	0	1	38	1	0	0	1	0
6192	Maruti Alto LXi	Coimbatore	64631	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	8	0	0	0	0	1	38	1	1	0	0	0
6280	Maruti Alto LXi	Jaipur	40000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	7	0	0	0	0	1	38	1	0	0	1	0
6684	Maruti Alto LXi	Chennai	60000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	9	0	0	0	0	1	38	1	1	0	0	0
6746	Maruti Alto LXi	Chennai	94385	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	10	0	0	0	0	1	38	1	0	0	1	0
6803	Maruti Alto LXi	Jaipur	65000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	12	0	0	0	0	1	38	1	0	0	1	0
6838	Maruti Alto LXi	Jaipur	90000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	9	0	0	0	0	1	38	1	1	0	0	0
7137	Maruti Alto LXi	Kolkata	39008	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	12	0	0	0	0	1	38	1	1	0	0	0
7165	Maruti Alto LXi	Hyderabad	62659	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	11	0	0	0	0	1	38	1	1	0	0	0
7193	Maruti Alto LXi	Jaipur	75000	796.00	46.3 bhp	5.00	4.36	NaN	Maruti	14	0	0	0	0	1	38	1	1	0	0	0

We will abort Option 2 and 3 because they will add a lot of complexity to the dataset and probably yield undesired results.

Statistics After Dropping Nulls

usedcarworksprice_df.describe().T

	count	mean	std	min	25%	50%	75%	max
Kilometers_Driven	6018.00	57668.05	37878.78	171.00	34000.00	53000.00	73000.00	775000.00
Engine	6018.00	1626.55	612.85	72.00	1198.00	1493.00	1984.00	5998.00
Seats	6018.00	5.28	0.81	0.00	5.00	5.00	5.00	10.00
New_Price	6018.00	21.49	24.33	3.91	7.88	11.30	21.77	375.00
Price	6018.00	9.47	11.17	0.44	3.50	5.64	9.95	160.00
Age	6018.00	6.64	3.27	1.00	4.00	6.00	9.00	22.00
Fuel_Type_CNG	6018.00	0.01	0.10	0.00	0.00	0.00	0.00	1.00
Fuel_Type_Diesel	6018.00	0.53	0.50	0.00	0.00	1.00	1.00	1.00
Fuel_Type_Electric	6018.00	0.00	0.02	0.00	0.00	0.00	0.00	1.00
Fuel_Type_LPG	6018.00	0.00	0.04	0.00	0.00	0.00	0.00	1.00
Fuel_Type_Petrol	6018.00	0.46	0.50	0.00	0.00	0.00	1.00	1.00
Mileage	6018.00	120.61	93.13	0.00	50.00	97.00	168.00	431.00
Transmission_Manual	6018.00	0.71	0.45	0.00	0.00	1.00	1.00	1.00
Owner_Type_First	6018.00	0.82	0.39	0.00	1.00	1.00	1.00	1.00
Owner_Type_Fourth & Above	6018.00	0.00	0.04	0.00	0.00	0.00	0.00	1.00
Owner_Type_Second	6018.00	0.16	0.37	0.00	0.00	0.00	0.00	1.00
Owner_Type_Third	6018.00	0.02	0.14	0.00	0.00	0.00	0.00	1.00

usedcarworksprice_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6018 entries, 0 to 5270
Data columns (total 21 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Name                       6018 non-null   object 
 1   Location                   6018 non-null   object 
 2   Kilometers_Driven          6018 non-null   int64  
 3   Engine                     6018 non-null   float64
 4   Power                      6018 non-null   object 
 5   Seats                      6018 non-null   float64
 6   New_Price                  6018 non-null   float64
 7   Price                      6018 non-null   float64
 8   Brand                      6018 non-null   object 
 9   Age                        6018 non-null   int64  
 10  Fuel_Type_CNG              6018 non-null   uint8  
 11  Fuel_Type_Diesel           6018 non-null   uint8  
 12  Fuel_Type_Electric         6018 non-null   uint8  
 13  Fuel_Type_LPG              6018 non-null   uint8  
 14  Fuel_Type_Petrol           6018 non-null   uint8  
 15  Mileage                    6018 non-null   int64  
 16  Transmission_Manual        6018 non-null   uint8  
 17  Owner_Type_First           6018 non-null   uint8  
 18  Owner_Type_Fourth & Above  6018 non-null   uint8  
 19  Owner_Type_Second          6018 non-null   uint8  
 20  Owner_Type_Third           6018 non-null   uint8  
dtypes: float64(4), int64(3), object(4), uint8(10)
memory usage: 623.0+ KB

Change Categoricals to Numeericals

#change "Mileage", "Power" from categorical to float
usedcarworksprice_df['Mileage'] = pd.factorize(usedcarworksprice_df['Mileage'])[0]
usedcarworksprice_df['Power'] = pd.factorize(usedcarworksprice_df['Power'])[0]

usedcarworksprice_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6018 entries, 0 to 5270
Data columns (total 21 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Name                       6018 non-null   object 
 1   Location                   6018 non-null   object 
 2   Kilometers_Driven          6018 non-null   int64  
 3   Engine                     6018 non-null   float64
 4   Power                      6018 non-null   int64  
 5   Seats                      6018 non-null   float64
 6   New_Price                  6018 non-null   float64
 7   Price                      6018 non-null   float64
 8   Brand                      6018 non-null   object 
 9   Age                        6018 non-null   int64  
 10  Fuel_Type_CNG              6018 non-null   uint8  
 11  Fuel_Type_Diesel           6018 non-null   uint8  
 12  Fuel_Type_Electric         6018 non-null   uint8  
 13  Fuel_Type_LPG              6018 non-null   uint8  
 14  Fuel_Type_Petrol           6018 non-null   uint8  
 15  Mileage                    6018 non-null   int64  
 16  Transmission_Manual        6018 non-null   uint8  
 17  Owner_Type_First           6018 non-null   uint8  
 18  Owner_Type_Fourth & Above  6018 non-null   uint8  
 19  Owner_Type_Second          6018 non-null   uint8  
 20  Owner_Type_Third           6018 non-null   uint8  
dtypes: float64(4), int64(4), object(3), uint8(10)
memory usage: 623.0+ KB

usedcarworksprice_df.describe().T

	count	mean	std	min	25%	50%	75%	max
Kilometers_Driven	6018.00	57668.05	37878.78	171.00	34000.00	53000.00	73000.00	775000.00
Engine	6018.00	1626.55	612.85	72.00	1198.00	1493.00	1984.00	5998.00
Power	6018.00	96.03	87.36	0.00	29.00	70.00	133.00	408.00
Seats	6018.00	5.28	0.81	0.00	5.00	5.00	5.00	10.00
New_Price	6018.00	21.49	24.33	3.91	7.88	11.30	21.77	375.00
Price	6018.00	9.47	11.17	0.44	3.50	5.64	9.95	160.00
Age	6018.00	6.64	3.27	1.00	4.00	6.00	9.00	22.00
Fuel_Type_CNG	6018.00	0.01	0.10	0.00	0.00	0.00	0.00	1.00
Fuel_Type_Diesel	6018.00	0.53	0.50	0.00	0.00	1.00	1.00	1.00
Fuel_Type_Electric	6018.00	0.00	0.02	0.00	0.00	0.00	0.00	1.00
Fuel_Type_LPG	6018.00	0.00	0.04	0.00	0.00	0.00	0.00	1.00
Fuel_Type_Petrol	6018.00	0.46	0.50	0.00	0.00	0.00	1.00	1.00
Mileage	6018.00	121.04	93.15	0.00	50.00	97.00	171.75	431.00
Transmission_Manual	6018.00	0.71	0.45	0.00	0.00	1.00	1.00	1.00
Owner_Type_First	6018.00	0.82	0.39	0.00	1.00	1.00	1.00	1.00
Owner_Type_Fourth & Above	6018.00	0.00	0.04	0.00	0.00	0.00	0.00	1.00
Owner_Type_Second	6018.00	0.16	0.37	0.00	0.00	0.00	0.00	1.00
Owner_Type_Third	6018.00	0.02	0.14	0.00	0.00	0.00	0.00	1.00

Distributions

#Distribution of Price

sns.displot(data=usedcarworksprice_df, x='Price', kind='kde')
plt.title("Used Car Price Distribution", fontsize=15)

Text(0.5, 1.0, 'Used Car Price Distribution')

#Distribution of New Price
sns.displot(data=usedcarworksprice_df, x='New_Price', kind='kde')
plt.title("New Car Price Distribution", fontsize=15)

Text(0.5, 1.0, 'New Car Price Distribution')

#Plot new price vs used price

#Distribution of Years
sns.displot(data=usedcarworksprice_df, x='Age', kind='kde')
plt.title("Car Age Distribution", fontsize=15)

Text(0.5, 1.0, 'Car Age Distribution')

#Distribution of Mileage
sns.displot(data=usedcarworksprice_df, x='Mileage', kind='kde')
plt.title("Car Mileage-Fuel Unit (Consumption) Distribution", fontsize=15)

Text(0.5, 1.0, 'Car Mileage-Fuel Unit (Consumption) Distribution')

#engine distribution
sns.displot(data=usedcarworksprice_df, x='Engine', kind='kde')
plt.title("Car Engine Size Distribution", fontsize=15)

Text(0.5, 1.0, 'Car Engine Size Distribution')

#Power Distribution
sns.displot(data=usedcarworksprice_df, x='Power', kind='kde')
plt.title("Car Engine Power Distribution", fontsize=15)

Text(0.5, 1.0, 'Car Engine Power Distribution')

plt.figure(figsize=(15, 7))
sns.heatmap(
    usedcarworksprice_df.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.show()

#heatmap to visualize correlation for price and common features
plt.figure(figsize=(15,7))
sns.heatmap(

usedcarworksprice_df[['Price','Engine','Power','Kilometers_Driven','Transmission_Manual','Seats','Mileage','Age','New_Price']].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.title("Correlation between Price and Common Features", fontsize=15)
plt.show()

High Correlation (+/-1 ~ +/- 0.5)

• New Price 0.76
• Engine 0.64
• Transmission_Manual -0.59
• Age -0.31

Medium Correlation (+/- 0.3 ~ +/- 0.49)

Low Correlation (+/- 0.29 and below)

• Mileage 0.18
• Kilometers_Driven -0.17
• Power 0.10
• Seats 0.05

plt.figure(figsize=(15,7))
sns.heatmap(

usedcarworksprice_df[['Price','Fuel_Type_Petrol','Fuel_Type_CNG','Fuel_Type_LPG','Fuel_Type_Diesel','Fuel_Type_Electric']].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.title("Correlation between Price and Fuel Types", fontsize=15)
plt.show()

High Correlation (+/-1 ~ +/- 0.5)

• none

Medium Correlation (+/- 0.3 ~ +/- 0.49)

• Fuel_Type_Diesel 0.32
• Fuel_Type_Petrol -0.31

Low Correlation (+/- 0.29 and below)

• All others

plt.figure(figsize=(15,7))
sns.heatmap(

usedcarworksprice_df[['Price','Owner_Type_First','Owner_Type_Second','Owner_Type_Third','Owner_Type_Fourth & Above']].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.title("Correlation between Price and Ownership", fontsize=15)
plt.show()

High Correlation (+/-1 ~ +/- 0.5)

• none

Medium Correlation (+/- 0.3 ~ +/- 0.49)

• none

Low Correlation (+/- 0.29 and below)

• all

plt.figure(figsize=(40, 60))
sns.pairplot(usedcarworksprice_df, y_vars="Price")
plt.show()

<Figure size 4000x6000 with 0 Axes>

Revisit Name and Location

usedcarworksprice_df.head()

	Name	Location	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Brand	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third
0	Maruti Wagon R LXI CNG	Mumbai	72000	998.00	0	5.00	5.51	1.75	Maruti	10	1	0	0	0	0	0	1	1	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	Pune	41000	1582.00	1	5.00	16.06	12.50	Hyundai	5	0	1	0	0	0	1	1	1	0	0	0
2	Honda Jazz V	Chennai	46000	1199.00	2	5.00	8.61	4.50	Honda	9	0	0	0	0	1	2	1	1	0	0	0
3	Maruti Ertiga VDI	Chennai	87000	1248.00	3	7.00	11.27	6.00	Maruti	8	0	1	0	0	0	3	1	1	0	0	0
4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	40670	1968.00	4	5.00	53.14	17.74	Audi	7	0	1	0	0	0	4	0	0	0	1	0

#How many brands?
print("There are ", usedcarworksprice_df['Brand'].nunique(), "car brands")

usedcarworksprice_df['Brand'].value_counts()

There are  31 car brands

Maruti           1211
Hyundai          1107
Honda             608
Toyota            411
Mercedes-Benz     318
Volkswagen        315
Ford              300
Mahindra          272
BMW               266
Audi              236
Tata              186
Skoda             173
Renault           145
Chevrolet         121
Nissan             91
Land Rover         60
Jaguar             40
Fiat               28
Mitsubishi         27
Mini               26
Volvo              21
Porsche            18
Jeep               15
Datsun             13
Force               3
ISUZU               2
Ambassador          1
Isuzu               1
Bentley             1
Lamborghini         1
Smart               1
Name: Brand, dtype: int64

#convert the locations to dummies, dont drop
#Convert Brand, don't drop any
usedcarworksprice_df = pd.get_dummies(usedcarworksprice_df, columns=['Location'])
usedcarworksprice_df = pd.get_dummies(usedcarworksprice_df, columns=['Brand'])
usedcarworksprice_df

	Name	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third	Location_Ahmedabad	Location_Bangalore	Location_Chennai	Location_Coimbatore	Location_Delhi	Location_Hyderabad	Location_Jaipur	Location_Kochi	Location_Kolkata	Location_Mumbai	Location_Pune	Brand_Ambassador	Brand_Audi	Brand_BMW	Brand_Bentley	Brand_Chevrolet	Brand_Datsun	Brand_Fiat	Brand_Force	Brand_Ford	Brand_Honda	Brand_Hyundai	Brand_ISUZU	Brand_Isuzu	Brand_Jaguar	Brand_Jeep	Brand_Lamborghini	Brand_Land Rover	Brand_Mahindra	Brand_Maruti	Brand_Mercedes-Benz	Brand_Mini	Brand_Mitsubishi	Brand_Nissan	Brand_Porsche	Brand_Renault	Brand_Skoda	Brand_Smart	Brand_Tata	Brand_Toyota	Brand_Volkswagen	Brand_Volvo
0	Maruti Wagon R LXI CNG	72000	998.00	0	5.00	5.51	1.75	10	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
1	Hyundai Creta 1.6 CRDi SX Option	41000	1582.00	1	5.00	16.06	12.50	5	0	1	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	Honda Jazz V	46000	1199.00	2	5.00	8.61	4.50	9	0	0	0	0	1	2	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	Maruti Ertiga VDI	87000	1248.00	3	7.00	11.27	6.00	8	0	1	0	0	0	3	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
4	Audi A4 New 2.0 TDI Multitronic	40670	1968.00	4	5.00	53.14	17.74	7	0	1	0	0	0	4	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4697	Fiat Punto 1.2 Dynamic	17941	1200.00	407	5.00	8.60	3.93	3	0	0	0	0	1	327	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4952	Fiat Punto 1.4 Emotion	47000	1400.00	408	5.00	8.60	1.49	10	0	0	0	0	1	209	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
5015	Maruti Swift 1.3 VXi	63000	1300.00	380	5.00	7.88	1.60	14	0	0	0	0	1	181	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
5185	Maruti Swift 1.3 LXI	52000	1300.00	380	5.00	7.88	3.65	8	0	0	0	0	1	181	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
5270	Honda City 1.5 GXI	53000	1500.00	403	5.00	13.58	1.85	18	0	0	0	0	1	14	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

6018 rows × 61 columns

plt.figure(figsize=(15,7))
sns.heatmap(

usedcarworksprice_df[['Price','Location_Ahmedabad','Location_Bangalore','Location_Chennai','Location_Coimbatore','Location_Delhi','Location_Hyderabad','Location_Jaipur','Location_Kochi','Location_Kolkata','Location_Mumbai','Location_Pune']].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.title("Correlation between Price and Location", fontsize=15)
plt.show()

High Correlation (+/-1 ~ +/- 0.5)

• none

Medium Correlation (+/- 0.3 ~ +/- 0.49)

• none

Low Correlation (+/- 0.29 and below)

• all

Location doesn't seem to contribute to Price

#visualize featues now including brands
usedcarworksprice_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6018 entries, 0 to 5270
Data columns (total 61 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Name                       6018 non-null   object 
 1   Kilometers_Driven          6018 non-null   int64  
 2   Engine                     6018 non-null   float64
 3   Power                      6018 non-null   int64  
 4   Seats                      6018 non-null   float64
 5   New_Price                  6018 non-null   float64
 6   Price                      6018 non-null   float64
 7   Age                        6018 non-null   int64  
 8   Fuel_Type_CNG              6018 non-null   uint8  
 9   Fuel_Type_Diesel           6018 non-null   uint8  
 10  Fuel_Type_Electric         6018 non-null   uint8  
 11  Fuel_Type_LPG              6018 non-null   uint8  
 12  Fuel_Type_Petrol           6018 non-null   uint8  
 13  Mileage                    6018 non-null   int64  
 14  Transmission_Manual        6018 non-null   uint8  
 15  Owner_Type_First           6018 non-null   uint8  
 16  Owner_Type_Fourth & Above  6018 non-null   uint8  
 17  Owner_Type_Second          6018 non-null   uint8  
 18  Owner_Type_Third           6018 non-null   uint8  
 19  Location_Ahmedabad         6018 non-null   uint8  
 20  Location_Bangalore         6018 non-null   uint8  
 21  Location_Chennai           6018 non-null   uint8  
 22  Location_Coimbatore        6018 non-null   uint8  
 23  Location_Delhi             6018 non-null   uint8  
 24  Location_Hyderabad         6018 non-null   uint8  
 25  Location_Jaipur            6018 non-null   uint8  
 26  Location_Kochi             6018 non-null   uint8  
 27  Location_Kolkata           6018 non-null   uint8  
 28  Location_Mumbai            6018 non-null   uint8  
 29  Location_Pune              6018 non-null   uint8  
 30  Brand_Ambassador           6018 non-null   uint8  
 31  Brand_Audi                 6018 non-null   uint8  
 32  Brand_BMW                  6018 non-null   uint8  
 33  Brand_Bentley              6018 non-null   uint8  
 34  Brand_Chevrolet            6018 non-null   uint8  
 35  Brand_Datsun               6018 non-null   uint8  
 36  Brand_Fiat                 6018 non-null   uint8  
 37  Brand_Force                6018 non-null   uint8  
 38  Brand_Ford                 6018 non-null   uint8  
 39  Brand_Honda                6018 non-null   uint8  
 40  Brand_Hyundai              6018 non-null   uint8  
 41  Brand_ISUZU                6018 non-null   uint8  
 42  Brand_Isuzu                6018 non-null   uint8  
 43  Brand_Jaguar               6018 non-null   uint8  
 44  Brand_Jeep                 6018 non-null   uint8  
 45  Brand_Lamborghini          6018 non-null   uint8  
 46  Brand_Land Rover           6018 non-null   uint8  
 47  Brand_Mahindra             6018 non-null   uint8  
 48  Brand_Maruti               6018 non-null   uint8  
 49  Brand_Mercedes-Benz        6018 non-null   uint8  
 50  Brand_Mini                 6018 non-null   uint8  
 51  Brand_Mitsubishi           6018 non-null   uint8  
 52  Brand_Nissan               6018 non-null   uint8  
 53  Brand_Porsche              6018 non-null   uint8  
 54  Brand_Renault              6018 non-null   uint8  
 55  Brand_Skoda                6018 non-null   uint8  
 56  Brand_Smart                6018 non-null   uint8  
 57  Brand_Tata                 6018 non-null   uint8  
 58  Brand_Toyota               6018 non-null   uint8  
 59  Brand_Volkswagen           6018 non-null   uint8  
 60  Brand_Volvo                6018 non-null   uint8  
dtypes: float64(4), int64(4), object(1), uint8(52)
memory usage: 904.8+ KB

#lets save car name in another dataframe for later use
carname_df = usedcarworksprice_df['Name']
carname_df

0                 Maruti Wagon R LXI CNG
1       Hyundai Creta 1.6 CRDi SX Option
2                           Honda Jazz V
3                      Maruti Ertiga VDI
4        Audi A4 New 2.0 TDI Multitronic
                      ...               
4697              Fiat Punto 1.2 Dynamic
4952              Fiat Punto 1.4 Emotion
5015                Maruti Swift 1.3 VXi
5185                Maruti Swift 1.3 LXI
5270                  Honda City 1.5 GXI
Name: Name, Length: 6018, dtype: object

#drop the name of the car
usedcarworksprice_df.drop('Name', axis=1, inplace=True)
usedcarworksprice_df.head()

	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third	Location_Ahmedabad	Location_Bangalore	Location_Chennai	Location_Coimbatore	Location_Delhi	Location_Hyderabad	Location_Jaipur	Location_Kochi	Location_Kolkata	Location_Mumbai	Location_Pune	Brand_Ambassador	Brand_Audi	Brand_BMW	Brand_Bentley	Brand_Chevrolet	Brand_Datsun	Brand_Fiat	Brand_Force	Brand_Ford	Brand_Honda	Brand_Hyundai	Brand_ISUZU	Brand_Isuzu	Brand_Jaguar	Brand_Jeep	Brand_Lamborghini	Brand_Land Rover	Brand_Mahindra	Brand_Maruti	Brand_Mercedes-Benz	Brand_Mini	Brand_Mitsubishi	Brand_Nissan	Brand_Porsche	Brand_Renault	Brand_Skoda	Brand_Smart	Brand_Tata	Brand_Toyota	Brand_Volkswagen	Brand_Volvo
0	72000	998.00	0	5.00	5.51	1.75	10	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
1	41000	1582.00	1	5.00	16.06	12.50	5	0	1	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	46000	1199.00	2	5.00	8.61	4.50	9	0	0	0	0	1	2	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	87000	1248.00	3	7.00	11.27	6.00	8	0	1	0	0	0	3	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
4	40670	1968.00	4	5.00	53.14	17.74	7	0	1	0	0	0	4	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

#correlation between price and brands

plt.figure(figsize=(25,25))
sns.heatmap(

usedcarworksprice_df[['Price','Brand_BMW','Brand_Bentley','Brand_Toyota','Brand_Hyundai','Brand_Ford','Brand_Datsun','Brand_Mini',
                      'Brand_Nissan','Brand_Mitsubishi','Brand_Isuzu','Brand_ISUZU','Brand_Honda','Brand_Ambassador','Brand_Audi',
                      'Brand_Maruti','Brand_Mahindra','Brand_Jaguar','Brand_Jeep','Brand_Maruti','Brand_Chevrolet','Brand_Mercedes-Benz',
                      'Brand_Land Rover','Brand_Force','Brand_Fiat','Brand_Lamborghini','Brand_Jeep','Brand_Smart',
                     'Brand_Volvo','Brand_Volkswagen','Brand_Tata','Brand_Skoda', 'Brand_Renault', 'Brand_Porsche']
                     ].corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.title("Correlation between Price and Brand", fontsize=15)
plt.show()

High Correlation (+/-1 ~ +/- 0.5)

• none

Medium Correlation (+/- 0.3 ~ +/- 0.49)

• Mercedes Benz

Low Correlation (+/- 0.29 and below)

• All others

These seem to add noise instead

Scaling

The question of scaling. Is it necessary?

If we do not scale, how will a Price error look like, since we have a Max price of 160 and a min price of 0.44?

from sklearn.preprocessing import StandardScaler

std_scaler = StandardScaler()

usedcarworksprice_df['Price'].hist(bins=20)
plt.title('Price before z transformation')
plt.show()
# fit_transform requires a DataFrame, not a Series, hence
# the double brackets to keep usedcarworksprice_df[['Price']] as a 1 column
# DataFrame rather than a Series, like if I did usedcarworksprice_df['Price']


usedcarworksprice_df['price_z_std'] = std_scaler.fit_transform(usedcarworksprice_df[['Price']])
usedcarworksprice_df['price_z_std'].hist(bins=20)
plt.title('Price after z transformation')
plt.show()
# exact same shape since it's a linear transformation.
usedcarworksprice_df['Price'] = usedcarworksprice_df['price_z_std']
usedcarworksprice_df.drop(['price_z_std'], axis=1, inplace=True)

usedcarworksprice_df

	Kilometers_Driven	Engine	Power	Seats	New_Price	Price	Age	Fuel_Type_CNG	Fuel_Type_Diesel	Fuel_Type_Electric	Fuel_Type_LPG	Fuel_Type_Petrol	Mileage	Transmission_Manual	Owner_Type_First	Owner_Type_Fourth & Above	Owner_Type_Second	Owner_Type_Third	Location_Ahmedabad	Location_Bangalore	Location_Chennai	Location_Coimbatore	Location_Delhi	Location_Hyderabad	Location_Jaipur	Location_Kochi	Location_Kolkata	Location_Mumbai	Location_Pune	Brand_Ambassador	Brand_Audi	Brand_BMW	Brand_Bentley	Brand_Chevrolet	Brand_Datsun	Brand_Fiat	Brand_Force	Brand_Ford	Brand_Honda	Brand_Hyundai	Brand_ISUZU	Brand_Isuzu	Brand_Jaguar	Brand_Jeep	Brand_Lamborghini	Brand_Land Rover	Brand_Mahindra	Brand_Maruti	Brand_Mercedes-Benz	Brand_Mini	Brand_Mitsubishi	Brand_Nissan	Brand_Porsche	Brand_Renault	Brand_Skoda	Brand_Smart	Brand_Tata	Brand_Toyota	Brand_Volkswagen	Brand_Volvo
0	72000	998.00	0	5.00	5.51	-0.69	10	1	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
1	41000	1582.00	1	5.00	16.06	0.27	5	0	1	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
2	46000	1199.00	2	5.00	8.61	-0.45	9	0	0	0	0	1	2	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
3	87000	1248.00	3	7.00	11.27	-0.31	8	0	1	0	0	0	3	1	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
4	40670	1968.00	4	5.00	53.14	0.74	7	0	1	0	0	0	4	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4697	17941	1200.00	407	5.00	8.60	-0.50	3	0	0	0	0	1	327	1	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
4952	47000	1400.00	408	5.00	8.60	-0.71	10	0	0	0	0	1	209	1	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
5015	63000	1300.00	380	5.00	7.88	-0.70	14	0	0	0	0	1	181	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
5185	52000	1300.00	380	5.00	7.88	-0.52	8	0	0	0	0	1	181	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
5270	53000	1500.00	403	5.00	13.58	-0.68	18	0	0	0	0	1	14	1	0	0	1	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

6018 rows × 60 columns

Training and Testing

Split the data into train and test

#which features are important
usedcarworksprice_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6018 entries, 0 to 5270
Data columns (total 60 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Kilometers_Driven          6018 non-null   int64  
 1   Engine                     6018 non-null   float64
 2   Power                      6018 non-null   int64  
 3   Seats                      6018 non-null   float64
 4   New_Price                  6018 non-null   float64
 5   Price                      6018 non-null   float64
 6   Age                        6018 non-null   int64  
 7   Fuel_Type_CNG              6018 non-null   uint8  
 8   Fuel_Type_Diesel           6018 non-null   uint8  
 9   Fuel_Type_Electric         6018 non-null   uint8  
 10  Fuel_Type_LPG              6018 non-null   uint8  
 11  Fuel_Type_Petrol           6018 non-null   uint8  
 12  Mileage                    6018 non-null   int64  
 13  Transmission_Manual        6018 non-null   uint8  
 14  Owner_Type_First           6018 non-null   uint8  
 15  Owner_Type_Fourth & Above  6018 non-null   uint8  
 16  Owner_Type_Second          6018 non-null   uint8  
 17  Owner_Type_Third           6018 non-null   uint8  
 18  Location_Ahmedabad         6018 non-null   uint8  
 19  Location_Bangalore         6018 non-null   uint8  
 20  Location_Chennai           6018 non-null   uint8  
 21  Location_Coimbatore        6018 non-null   uint8  
 22  Location_Delhi             6018 non-null   uint8  
 23  Location_Hyderabad         6018 non-null   uint8  
 24  Location_Jaipur            6018 non-null   uint8  
 25  Location_Kochi             6018 non-null   uint8  
 26  Location_Kolkata           6018 non-null   uint8  
 27  Location_Mumbai            6018 non-null   uint8  
 28  Location_Pune              6018 non-null   uint8  
 29  Brand_Ambassador           6018 non-null   uint8  
 30  Brand_Audi                 6018 non-null   uint8  
 31  Brand_BMW                  6018 non-null   uint8  
 32  Brand_Bentley              6018 non-null   uint8  
 33  Brand_Chevrolet            6018 non-null   uint8  
 34  Brand_Datsun               6018 non-null   uint8  
 35  Brand_Fiat                 6018 non-null   uint8  
 36  Brand_Force                6018 non-null   uint8  
 37  Brand_Ford                 6018 non-null   uint8  
 38  Brand_Honda                6018 non-null   uint8  
 39  Brand_Hyundai              6018 non-null   uint8  
 40  Brand_ISUZU                6018 non-null   uint8  
 41  Brand_Isuzu                6018 non-null   uint8  
 42  Brand_Jaguar               6018 non-null   uint8  
 43  Brand_Jeep                 6018 non-null   uint8  
 44  Brand_Lamborghini          6018 non-null   uint8  
 45  Brand_Land Rover           6018 non-null   uint8  
 46  Brand_Mahindra             6018 non-null   uint8  
 47  Brand_Maruti               6018 non-null   uint8  
 48  Brand_Mercedes-Benz        6018 non-null   uint8  
 49  Brand_Mini                 6018 non-null   uint8  
 50  Brand_Mitsubishi           6018 non-null   uint8  
 51  Brand_Nissan               6018 non-null   uint8  
 52  Brand_Porsche              6018 non-null   uint8  
 53  Brand_Renault              6018 non-null   uint8  
 54  Brand_Skoda                6018 non-null   uint8  
 55  Brand_Smart                6018 non-null   uint8  
 56  Brand_Tata                 6018 non-null   uint8  
 57  Brand_Toyota               6018 non-null   uint8  
 58  Brand_Volkswagen           6018 non-null   uint8  
 59  Brand_Volvo                6018 non-null   uint8  
dtypes: float64(4), int64(4), uint8(52)
memory usage: 857.8 KB

#Training and Testing
X= usedcarworksprice_df.drop(["Price"],axis = 1)
y= usedcarworksprice_df['Price']
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

print("Number of rows in train data =", x_train.shape[0])
print("Number of rows in test data =", x_test.shape[0])

Number of rows in train data = 4212
Number of rows in test data = 1806

lin_reg_model = LinearRegression()
lin_reg_model.fit(x_train, y_train)

LinearRegression()

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# function to compute adjusted R-squared
def adj_r2_score(predictors, targets, predictions):
    r2 = r2_score(targets, predictions)
    n = predictors.shape[0]
    k = predictors.shape[1]
    return 1 - ((1 - r2) * (n - 1) / (n - k - 1))


# function to compute MAPE
def mape_score(targets, predictions):
    return np.mean(np.abs(targets - predictions) / targets) * 100


# function to compute different metrics to check performance of a regression model
def model_performance_regression(model, predictors, target):
    """
    Function to compute different metrics to check regression model performance

    model: regressor
    predictors: independent variables
    target: dependent variable
    """

    # predicting using the independent variables
    pred = model.predict(predictors)

    r2 = r2_score(target, pred)  # to compute R-squared
    adjr2 = adj_r2_score(predictors, target, pred)  # to compute adjusted R-squared
    rmse = np.sqrt(mean_squared_error(target, pred))  # to compute RMSE
    mae = mean_absolute_error(target, pred)  # to compute MAE
    #mape = mape_score(target, pred)  # to compute MAPE

    # creating a dataframe of metrics
    df_perf = pd.DataFrame(
        {
            "RMSE": rmse,
            "MAE": mae,
            "R-squared": r2,
            "Adj. R-squared": adjr2,
            #"MAPE": mape,
        },
        index=[0],
    )

    return df_perf

# Checking model performance on train set
print("Training Performance\n")
lin_reg_model_train_perf = model_performance_regression(lin_reg_model, x_train, y_train)
lin_reg_model_train_perf

Training Performance

	RMSE	MAE	R-squared	Adj. R-squared
0	0.48	0.27	0.78	0.77

# Checking model performance on test set
print("Test Performance\n")
lin_reg_model_test_perf = model_performance_regression(lin_reg_model, x_test, y_test)
lin_reg_model_test_perf

Test Performance

	RMSE	MAE	R-squared	Adj. R-squared
0	0.46	0.26	0.76	0.76

• Train and test R2 are 0.77 and 0.76, which means that the model explains 77% and 76% of the total variation in the train and test sets respectively. Also both scores are comparable.

• RMSE scores in both training and testing sets are comparable, which means that the model is not overfitting.

• MAE, our model is able to predict prices within a mean error of 0.27/0.26, which is about 27% and 26%.

The overall performance is bad.

Rethinking the features to train

Maybe some of the features we added only add noise to our model. What if we remove locations and brands?

#updated dataset
usedcarworksprice_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6018 entries, 0 to 5270
Data columns (total 60 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Kilometers_Driven          6018 non-null   int64  
 1   Engine                     6018 non-null   float64
 2   Power                      6018 non-null   int64  
 3   Seats                      6018 non-null   float64
 4   New_Price                  6018 non-null   float64
 5   Price                      6018 non-null   float64
 6   Age                        6018 non-null   int64  
 7   Fuel_Type_CNG              6018 non-null   uint8  
 8   Fuel_Type_Diesel           6018 non-null   uint8  
 9   Fuel_Type_Electric         6018 non-null   uint8  
 10  Fuel_Type_LPG              6018 non-null   uint8  
 11  Fuel_Type_Petrol           6018 non-null   uint8  
 12  Mileage                    6018 non-null   int64  
 13  Transmission_Manual        6018 non-null   uint8  
 14  Owner_Type_First           6018 non-null   uint8  
 15  Owner_Type_Fourth & Above  6018 non-null   uint8  
 16  Owner_Type_Second          6018 non-null   uint8  
 17  Owner_Type_Third           6018 non-null   uint8  
 18  Location_Ahmedabad         6018 non-null   uint8  
 19  Location_Bangalore         6018 non-null   uint8  
 20  Location_Chennai           6018 non-null   uint8  
 21  Location_Coimbatore        6018 non-null   uint8  
 22  Location_Delhi             6018 non-null   uint8  
 23  Location_Hyderabad         6018 non-null   uint8  
 24  Location_Jaipur            6018 non-null   uint8  
 25  Location_Kochi             6018 non-null   uint8  
 26  Location_Kolkata           6018 non-null   uint8  
 27  Location_Mumbai            6018 non-null   uint8  
 28  Location_Pune              6018 non-null   uint8  
 29  Brand_Ambassador           6018 non-null   uint8  
 30  Brand_Audi                 6018 non-null   uint8  
 31  Brand_BMW                  6018 non-null   uint8  
 32  Brand_Bentley              6018 non-null   uint8  
 33  Brand_Chevrolet            6018 non-null   uint8  
 34  Brand_Datsun               6018 non-null   uint8  
 35  Brand_Fiat                 6018 non-null   uint8  
 36  Brand_Force                6018 non-null   uint8  
 37  Brand_Ford                 6018 non-null   uint8  
 38  Brand_Honda                6018 non-null   uint8  
 39  Brand_Hyundai              6018 non-null   uint8  
 40  Brand_ISUZU                6018 non-null   uint8  
 41  Brand_Isuzu                6018 non-null   uint8  
 42  Brand_Jaguar               6018 non-null   uint8  
 43  Brand_Jeep                 6018 non-null   uint8  
 44  Brand_Lamborghini          6018 non-null   uint8  
 45  Brand_Land Rover           6018 non-null   uint8  
 46  Brand_Mahindra             6018 non-null   uint8  
 47  Brand_Maruti               6018 non-null   uint8  
 48  Brand_Mercedes-Benz        6018 non-null   uint8  
 49  Brand_Mini                 6018 non-null   uint8  
 50  Brand_Mitsubishi           6018 non-null   uint8  
 51  Brand_Nissan               6018 non-null   uint8  
 52  Brand_Porsche              6018 non-null   uint8  
 53  Brand_Renault              6018 non-null   uint8  
 54  Brand_Skoda                6018 non-null   uint8  
 55  Brand_Smart                6018 non-null   uint8  
 56  Brand_Tata                 6018 non-null   uint8  
 57  Brand_Toyota               6018 non-null   uint8  
 58  Brand_Volkswagen           6018 non-null   uint8  
 59  Brand_Volvo                6018 non-null   uint8  
dtypes: float64(4), int64(4), uint8(52)
memory usage: 857.8 KB

#lets remove columns 18 to 52 as we discovered earlier that they do not contribute much.

improvedusedcar_df = usedcarworksprice_df.drop(['Brand_BMW','Brand_Bentley','Brand_Toyota','Brand_Hyundai','Brand_Ford','Brand_Datsun','Brand_Mini',
                      'Brand_Nissan','Brand_Mitsubishi','Brand_Isuzu','Brand_ISUZU','Brand_Honda','Brand_Ambassador','Brand_Audi',
                      'Brand_Maruti','Brand_Mahindra','Brand_Jaguar','Brand_Jeep','Brand_Maruti','Brand_Chevrolet','Brand_Mercedes-Benz',
                      'Brand_Land Rover','Brand_Force','Brand_Fiat','Brand_Lamborghini','Brand_Jeep','Brand_Smart',
                     'Brand_Volvo','Brand_Volkswagen','Brand_Tata','Brand_Skoda', 'Brand_Renault', 'Brand_Porsche','Location_Ahmedabad','Location_Bangalore','Location_Chennai','Location_Coimbatore','Location_Delhi','Location_Hyderabad','Location_Jaipur','Location_Kochi','Location_Kolkata','Location_Mumbai','Location_Pune'], axis=1)

improvedusedcar_df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 6018 entries, 0 to 5270
Data columns (total 18 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Kilometers_Driven          6018 non-null   int64  
 1   Engine                     6018 non-null   float64
 2   Power                      6018 non-null   int64  
 3   Seats                      6018 non-null   float64
 4   New_Price                  6018 non-null   float64
 5   Price                      6018 non-null   float64
 6   Age                        6018 non-null   int64  
 7   Fuel_Type_CNG              6018 non-null   uint8  
 8   Fuel_Type_Diesel           6018 non-null   uint8  
 9   Fuel_Type_Electric         6018 non-null   uint8  
 10  Fuel_Type_LPG              6018 non-null   uint8  
 11  Fuel_Type_Petrol           6018 non-null   uint8  
 12  Mileage                    6018 non-null   int64  
 13  Transmission_Manual        6018 non-null   uint8  
 14  Owner_Type_First           6018 non-null   uint8  
 15  Owner_Type_Fourth & Above  6018 non-null   uint8  
 16  Owner_Type_Second          6018 non-null   uint8  
 17  Owner_Type_Third           6018 non-null   uint8  
dtypes: float64(4), int64(4), uint8(10)
memory usage: 610.9 KB

Retry Our model

#Training and Testing
X= improvedusedcar_df.drop(["Price"],axis = 1)
y= improvedusedcar_df['Price']
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)

print("Number of rows in train data =", x_train.shape[0])
print("Number of rows in test data =", x_test.shape[0])

Number of rows in train data = 4212
Number of rows in test data = 1806

lin_reg_model = LinearRegression()
lin_reg_model.fit(x_train, y_train)

LinearRegression()

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# Checking model performance on train set
print("Training Performance\n")
lin_reg_model_train_perf = model_performance_regression(lin_reg_model, x_train, y_train)
lin_reg_model_train_perf

Training Performance

	RMSE	MAE	R-squared	Adj. R-squared
0	0.53	0.30	0.73	0.73

# Checking model performance on test set
print("Test Performance\n")
lin_reg_model_test_perf = model_performance_regression(lin_reg_model, x_test, y_test)
lin_reg_model_test_perf

Test Performance

	RMSE	MAE	R-squared	Adj. R-squared
0	0.48	0.29	0.74	0.74

Our model improved a little, since R2 was reduced.

Improve with Forward Feature Selection Using SequentialFeatureSelector

!pip install mlxtend

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!pip install joblib

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!pip install -U scikit-learn

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import joblib
import mlxtend
import sys
sys.modules['sklearn.externals.joblib'] = joblib

from mlxtend.feature_selection import SequentialFeatureSelector as SFS

reg = LinearRegression()

# Build step forward feature selection
sfs = SFS(
    reg,
    k_features=x_train.shape[1],
    forward=True,  # k_features denotes the number of features to select
    floating=False,
    scoring="r2",
    #n_jobs=-1,  # n_jobs=-1 means all processor cores will be used
    n_jobs=1,
    verbose=2,
    cv=5,
)

# Perform SFFS
sfs = sfs.fit(x_train, y_train)

[2023-09-01 12:06:13] Features: 1/17 -- score: 0.568674190349858
[2023-09-01 12:06:13] Features: 2/17 -- score: 0.6676532951612832
[2023-09-01 12:06:14] Features: 3/17 -- score: 0.7013201296876399
[2023-09-01 12:06:14] Features: 4/17 -- score: 0.7095267830336398
[2023-09-01 12:06:14] Features: 5/17 -- score: 0.7144905409344757
[2023-09-01 12:06:15] Features: 6/17 -- score: 0.7170957013883257
[2023-09-01 12:06:16] Features: 7/17 -- score: 0.7185880334284287
[2023-09-01 12:06:17] Features: 8/17 -- score: 0.719523468910801
[2023-09-01 12:06:17] Features: 9/17 -- score: 0.7201106144172472
[2023-09-01 12:06:18] Features: 10/17 -- score: 0.7201471367890765
[2023-09-01 12:06:19] Features: 11/17 -- score: 0.7201671094431836
[2023-09-01 12:06:19] Features: 12/17 -- score: 0.720164541274315
[2023-09-01 12:06:20] Features: 13/17 -- score: 0.7201074871162803
[2023-09-01 12:06:20] Features: 14/17 -- score: 0.7201074871162803
[2023-09-01 12:06:20] Features: 15/17 -- score: 0.7199409508111616
[2023-09-01 12:06:21] Features: 16/17 -- score: 0.7199409508111615
[2023-09-01 12:06:21] Features: 17/17 -- score: 0.719756228923259

# to plot the performance with addition of each feature
from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs
plot_sfs(sfs.get_metric_dict(), kind="std_err")
plt.title("Sequential Forward Selection (w. StdErr)")
plt.xticks(rotation=90)
plt.show()

We can see that performance increases till the 6th feature and then slowly becomes constant.

The decision to choose the k_features now depends on the adjusted R2 vs the complexity of the model.

With 7 features, we are getting an adjusted R2 of 0.719. With 12 features, we are getting an adjusted R2 of 0.72. With 16 features, we are getting an adjusted R2 of 0.72.

The increase in adjusted R2 is not very significant as we are getting the same values with a less complex model.

We will experiment with 17

reg = LinearRegression()

# Build step forward feature selection
sfs = SFS(
    reg,
    k_features=17,
    forward=True,
    floating=False,
    scoring="r2",
    n_jobs=1,
    verbose=2,
    cv=5,
)

# Perform SFFS
sfs = sfs.fit(x_train, y_train)

[2023-09-01 12:06:21] Features: 1/17 -- score: 0.568674190349858
[2023-09-01 12:06:22] Features: 2/17 -- score: 0.6676532951612832
[2023-09-01 12:06:22] Features: 3/17 -- score: 0.7013201296876399
[2023-09-01 12:06:22] Features: 4/17 -- score: 0.7095267830336398
[2023-09-01 12:06:23] Features: 5/17 -- score: 0.7144905409344757
[2023-09-01 12:06:23] Features: 6/17 -- score: 0.7170957013883257
[2023-09-01 12:06:23] Features: 7/17 -- score: 0.7185880334284287
[2023-09-01 12:06:24] Features: 8/17 -- score: 0.719523468910801
[2023-09-01 12:06:24] Features: 9/17 -- score: 0.7201106144172472
[2023-09-01 12:06:25] Features: 10/17 -- score: 0.7201471367890765
[2023-09-01 12:06:25] Features: 11/17 -- score: 0.7201671094431836
[2023-09-01 12:06:25] Features: 12/17 -- score: 0.720164541274315
[2023-09-01 12:06:26] Features: 13/17 -- score: 0.7201074871162803
[2023-09-01 12:06:26] Features: 14/17 -- score: 0.7201074871162803
[2023-09-01 12:06:26] Features: 15/17 -- score: 0.7199409508111616
[2023-09-01 12:06:26] Features: 16/17 -- score: 0.7199409508111615
[2023-09-01 12:06:26] Features: 17/17 -- score: 0.719756228923259

Remember the columns

0 Kilometers_Driven 6018 non-null int64

1 Engine 6018 non-null float64

2 Power 6018 non-null int64

3 Seats 6018 non-null float64

4 New_Price 6018 non-null float64

5 Price 6018 non-null float64

6 Age 6018 non-null int64

7 Fuel_Type_CNG 6018 non-null uint8

8 Fuel_Type_Diesel 6018 non-null uint8

9 Fuel_Type_Electric 6018 non-null uint8

10 Fuel_Type_LPG 6018 non-null uint8

11 Fuel_Type_Petrol 6018 non-null uint8

12 Mileage 6018 non-null int64

13 Transmission_Manual 6018 non-null uint8

14 Owner_Type_First 6018 non-null uint8

15 Owner_Type_Fourth & Above 6018 non-null uint8

16 Owner_Type_Second 6018 non-null uint8

17 Owner_Type_Third 6018 non-null uint8

# let us select the features which are important
#feat_cols = list(sfs.k_feature_idx_)
feat_cols = [1,4,6, 13]
print(feat_cols)

[1, 4, 6, 13]

# let us look at the names of the important features
x_train.columns[feat_cols]

Index(['Engine', 'New_Price', 'Fuel_Type_CNG', 'Owner_Type_First'], dtype='object')

x_train_final = x_train[x_train.columns[feat_cols]]

# Creating new x_test with the same 20 variables that we selected for x_train
x_test_final = x_test[x_train_final.columns]

# Fitting linear model
lin_reg_model2 = LinearRegression()
lin_reg_model2.fit(x_train_final, y_train)

LinearRegression()

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# model performance on train set
print("Training Performance\n")
lin_reg_model2_train_perf = model_performance_regression(
    lin_reg_model2, x_train_final, y_train
)
lin_reg_model2_train_perf

Training Performance

	RMSE	MAE	R-squared	Adj. R-squared
0	0.63	0.35	0.62	0.62

# model performance on test set
print("Test Performance\n")
lin_reg_model2_test_perf = model_performance_regression(
    lin_reg_model2, x_test_final, y_test
)
lin_reg_model2_test_perf

Test Performance

	RMSE	MAE	R-squared	Adj. R-squared
0	0.56	0.32	0.65	0.65

# training performance comparison

models_train_comp_df = pd.concat(
    [lin_reg_model_train_perf.T, lin_reg_model2_train_perf.T], axis=1,
)

models_train_comp_df.columns = [
    "Linear Regression sklearn",
    "Linear Regression sklearn (SFS features)",
]

print("Training performance comparison:")
models_train_comp_df

Training performance comparison:

	Linear Regression sklearn	Linear Regression sklearn (SFS features)
RMSE	0.53	0.63
MAE	0.30	0.35
R-squared	0.73	0.62
Adj. R-squared	0.73	0.62

# test performance comparison

models_test_comp_df = pd.concat(
    [lin_reg_model_test_perf.T, lin_reg_model2_test_perf.T], axis=1,
)

models_test_comp_df.columns = [
    "Linear Regression sklearn",
    "Linear Regression sklearn (SFS features)",
]

print("Test performance comparison:")
models_test_comp_df

Test performance comparison:

	Linear Regression sklearn	Linear Regression sklearn (SFS features)
RMSE	0.48	0.56
MAE	0.29	0.32
R-squared	0.74	0.65
Adj. R-squared	0.74	0.65

sns.pairplot(data=improvedusedcar_df, hue='Transmission_Manual', size=2.5);

Notice the following:

• Mean Squared Error(MSE) and Root Mean Square Error penalizes the large prediction errors vi-a-vis Mean Absolute Error (MAE). However, RMSE is widely used than MSE to evaluate the performance of the regression model with other random models as it has the same units as the dependent variable (Y-axis).

• The lower value of MAE, MSE, and RMSE implies higher accuracy of a regression model. However, a higher value of R square is considered desirable.

• R Squared & Adjusted R Squared are used for explaining how well the independent variables in the linear regression model explains the variability in the dependent variable. R Squared value always increases with the addition of the independent variables which might lead to the addition of the redundant variables in our model. However, the adjusted R-squared solves this problem.

• Adjusted R squared takes into account the number of predictor variables, and it is used to determine the number of independent variables in our model. The value of Adjusted R squared decreases if the increase in the R square by the additional variable isn’t significant enough.

• For comparing the accuracy among different linear regression models, RMSE is a better choice than R Squared.

While both RMSE and R- Squared quantifies how well a linear regression model fits a dataset, the RMSE tells how well a regression model can predict the value of a response variable in absolute terms and R- Squared tells how well the predictor variables can explain the variation in the response variable.

So, in our model we will be using sklearn Linear regression because it shows better peformance (Lower RMSE, Lower MAS, similar R2 and Ajdusted R2).

Conclusions and Recommendations

• We have built a predictive model that can be used by UCars to predict the price of a used car with an R2 of 0.74 on the training set.
• Cars4U can use this model to predict the price of used cars within a mean error of 0.29.
• From the analysis, we found that the original price of the car when new, odometer reading and the age of the car were the most important features to consider when pricing a car (although they show not to have a large correlation in the heat map). Less important was engine size.

• Higher original price when new, less km on the odometer, less years will increase the selling price of the used car. The opposite will reduce the price.

• When selling a used car keep in mind the following:

If the car had one, two, three, four or more owners, this will not affect the car price significantly.

Focus on the most important factors that affect the car price:

• Odometer reading
• Age of the car
• Original Price of the car when new

Customers may pay more for Diesel cars. Petrol is second. CNG, LPG, and Electric do not increase price.

Cars that have an medium Power sell higher than those underpoweered and overpowered.

Customers may pay more for Mercedes, Jaguar, BMW. But this may be related with the fact that these cars had the highest prices when new.

Locations (where the car is) and Brands don't really play a significant role on pricing.

We can also explore improving the linear model by applying non-linear transformations to some of the attributes. This might help us better identify the patterns in the data to predict the anime ratings more accurately.

Final Recommendations:

Focus on selling cars that have these things in common:

1. Were expensive when new.
2. Have lower miles on the odometer
3. Are less than 10 years old
4. Diesel and Petrol cars.
5. Have an engine size between 2000 and 4000cc
6. Customer may pay slightly more for Automatic Transmission cars.

Do not Focus so much on:

1. How many owners the car had before
2. Engine Power
3. Brand

Use our model to estimate the selling price but keep in mind that the model can only accurately has an R2 of 0.74.